Enhancing Images With Filtering

Enhancing Images with Filtering
Homomorphic filtering
Homomorphic filtering is a method used in image processing and enhancement. It's particularly
effective for improving the visibility of details in images where there's uneven illumination or where
there's a wide range of lighting conditions.
Here's a simplified explanation of how it works:
1. Frequency Domain Transformation: The image is converted from spatial domain to frequency
domain using techniques like the Fourier Transform. This transformation separates the image into
its frequency components.
2. Filtering: In the frequency domain, the image is filtered to adjust the amplitudes of different
frequency components. Homomorphic filtering applies a specific filter that amplifies the low-
frequency components (associated with illumination) and attenuates the high-frequency
components (associated with details).
3. Inverse Transformation: After filtering, the modified frequency domain representation of the
image is converted back to the spatial domain using techniques like the Inverse Fourier Transform.
This brings the image back to its original form but with the desired enhancements.
Homomorphic filtering is useful in various applications such as in medical imaging (like MRI and CT
scans), satellite imagery processing, and in forensic analysis where it helps in revealing hidden details in
images affected by uneven lighting or contrast.
What is Noise? Describe any two noise models in detail.
What is geometric transformation? Also discuss Euclidean transforination.
Noise in the context of image processing refers to random variations in pixel values that are not part of
the underlying image information. These variations can be caused by various factors such as sensor
limitations, transmission interference, or environmental conditions. Noise can degrade image quality,
making it harder to extract useful information or perform accurate analysis.
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Two common noise models are Gaussian noise and salt-and-pepper noise:
1. Gaussian Noise:
Gaussian noise follows a normal distribution and is characterized by its mean (μ) and
standard deviation (σ).
In an image, Gaussian noise adds random variations to pixel values, with higher intensities
near the mean value and decreasing intensity as the distance from the mean increases.
It is often caused by electronic components in imaging devices or electronic interference
during image transmission.
Gaussian noise is typically represented as:
f (x, y) = I(x, y) + n(x, y)
where I(x, y) is the original image intensity at position (x, y), and n(x, y) is the noise added at
that position.
2. Salt-and-Pepper Noise:
Salt-and-pepper noise is characterized by sporadic occurrence of very bright or very dark

pixels in an image.
It simulates the effect of random impulse noise, such as dead pixels in a camera sensor or
errors during image transmission.
In a grayscale image, salt-and-pepper noise manifests as isolated bright pixels (salt) and dark
pixels (pepper).
This type of noise can significantly degrade image quality and affect the performance of
algorithms that rely on accurate pixel values.
Salt-and-pepper noise is often represented as a probability of occurrence, where certain
pixels are randomly selected and set to either the maximum or minimum intensity value.
Now, regarding geometric transformations:
Geometric Transformation involves altering the spatial relationships between pixels in an image. It
includes operations like translation, rotation, scaling, and shearing. These transformations are applied
to the entire image or specific regions within the image to achieve desired effects like resizing,
reorienting, or correcting distortions.
Euclidean Transformation is a specific type of geometric transformation that preserves distances and
angles between points in an image. It includes translation (shifting), rotation, and reflection. Here's a
brief discussion of each component:
1. Translation: It involves shifting an image by a certain distance along the x and y axes. The
transformation matrix for translation is:
⎡ 1 0 tx ⎤
0 1 ty

⎣0 0 1 ⎦
where tx and ty are the translation distances along the x and y axes, respectively.

2. Rotation: It involves rotating an image around a fixed point (usually the origin) by a certain angle.
The transformation matrix for rotation is:
⎡ cos(θ) − sin(θ) 0 ⎤
sin(θ) cos(θ) 0
⎣ 0 1⎦

where θ is the angle of rotation.
3. Reflection: It involves flipping an image across a specified axis. For example, horizontal or vertical
reflection. The transformation matrix for reflection depends on the axis of reflection.
Euclidean transformations are fundamental in computer graphics, computer vision, and image
processing, allowing for various manipulations and corrections to be applied to images while preserving
essential geometric properties.
What are the different stages of DIP? Explain in detail.
What is the difference between image enhancement and image restoration? Mention some causes
of image degradation.
Digital Image Processing (DIP) involves a series of stages or steps to manipulate and analyze digital
images for various purposes. Here are the different stages typically involved in DIP:
1. Image Acquisition: This stage involves capturing digital images using imaging devices such as
cameras, scanners, or medical imaging equipment. The quality and characteristics of the acquired
images can vary based on factors like the sensor resolution, optics quality, and environmental
conditions.
2. Preprocessing: Preprocessing steps are applied to the raw acquired images to improve their
quality and prepare them for further analysis. Common preprocessing techniques include:
Noise Reduction: Removing or reducing noise introduced during image acquisition, which
can improve image clarity and quality.
Image Denoising: Using filters or algorithms to reduce the effects of noise while preserving
important image features.
Image Enhancement: Applying techniques to improve the visual quality of images by
adjusting contrast, brightness, or sharpness.
3. Image Segmentation: Image segmentation involves partitioning an image into meaningful

regions or segments based on similarities in pixel values, colors, textures, or other features.
Segmentation is useful for tasks like object detection, recognition, and analysis.
4. Feature Extraction: In this stage, relevant features or characteristics are extracted from the
segmented regions of the image. These features could include shapes, textures, colors, or other
properties that are important for subsequent analysis or classification tasks.
5. Image Representation and Description: This stage involves representing images in a suitable
format for further processing or analysis. It includes encoding images into appropriate data
structures or descriptors that capture their essential properties.
6. Image Analysis and Interpretation: Image analysis techniques are applied to extract meaningful
information from images and interpret their content. This could involve tasks such as object
detection, classification, recognition, or measurement.
7. Postprocessing: Postprocessing steps are applied to the processed images to refine the results or
prepare them for specific applications. This might include further noise reduction, image
enhancement, or formatting for display or storage.
8. Visualization and Interpretation: The final stage involves visualizing the processed images and
interpreting the results of the analysis. This could involve generating visualizations, reports, or
summaries to communicate the findings effectively.
Now, regarding the difference between image enhancement and image restoration:
Image Enhancement:
Image enhancement techniques are used to improve the visual quality of an image for human
perception or specific application requirements.
The goal of image enhancement is to highlight important features, improve contrast, reduce noise,
or sharpen details without fundamentally altering the underlying image content.
Enhancement techniques include adjusting brightness, contrast, color balance, and sharpness, as
well as applying filters for noise reduction or edge enhancement.
Image Restoration:
Image restoration involves the process of recovering or restoring the original, undegraded version
of an image from a degraded or distorted version.
The degradation in images can occur due to factors such as noise, blur, motion, or compression
artifacts.
The goal of image restoration is to reduce or remove the effects of degradation to improve the
fidelity and quality of the image.
Restoration techniques often involve sophisticated algorithms that attempt to model the
degradation process and reverse its effects to reconstruct the original image as accurately as
possible.
Causes of Image Degradation:
1. Noise: Random variations in pixel values caused by factors such as sensor limitations, electronic
interference, or environmental conditions.
2. Blur: Loss of sharpness or detail in an image due to factors such as motion blur, defocus blur, or
optical imperfections.
3. Compression Artifacts: Distortions introduced during image compression, which can result in
blockiness, ringing, or other visual artifacts.
4. Atmospheric Effects: Interference from atmospheric conditions such as haze, fog, or smoke,
which can reduce contrast and visibility.
5. Sensor Imperfections: Flaws in imaging sensors or optics that result in distortions, vignetting, or
other aberrations in captured images.
Explain the Laplacian image filtering algorithm
Explain histogram equalization. Equalize the given histogram:
Grey Level - 0 1 2 3 4 5 6 7
No. of Pixels- 790 1023 850 656 329 245 122 81
The Laplacian image filtering algorithm is a technique used in image processing for edge detection and
sharpening. It is based on the second derivative of the image intensity function and is particularly
effective in detecting regions of rapid intensity change, which typically correspond to edges in the
image.
Here's how the Laplacian filtering algorithm works:
1. Compute the Laplacian Kernel: The Laplacian kernel, also known as the Laplacian of Gaussian
(LoG) kernel, is a discrete approximation of the Laplacian operator. It is a second-order derivative
operator used for edge detection. The Laplacian kernel highlights regions of rapid intensity change
in the image.
2. Convolution: The Laplacian kernel is convolved with the input image. Convolution involves sliding
the kernel over the entire image and computing the sum of products between the kernel values
and corresponding pixel values in the image.
3. Edge Detection: After convolution, the resulting image contains high positive values at edges and
low values elsewhere. These high positive values indicate the presence of edges in the image.
4. Enhancement or Sharpening: The Laplacian-filtered image can be used for enhancing edges or
for sharpening the original image. One common approach is to add the Laplacian-filtered image to
the original image, effectively enhancing the edges and making them more prominent.
The Laplacian filter is sensitive to noise, so it is often applied after smoothing the image with a Gaussian
filter to reduce noise.
Now, for histogram equalization:
Histogram equalization is a technique used in image processing to enhance the contrast of an image by
redistributing the intensity values of the image's pixels. It aims to achieve a uniform distribution of pixel
intensities across the entire intensity range, thereby maximizing the use of available dynamic range and
making the image visually appealing.
Here's how histogram equalization works:
1. Compute Histogram: The first step is to calculate the histogram of the input image. The histogram
represents the frequency distribution of pixel intensities in the image.
2. Cumulative Distribution Function (CDF): Next, the cumulative distribution function (CDF) of the
histogram is computed. The CDF represents the cumulative sum of normalized histogram values.
3. Histogram Equalization Transformation: The histogram equalization transformation function is

then determined based on the CDF. This transformation function maps the original pixel intensities
to new intensity values such that the cumulative distribution of intensities becomes uniform.
4. Apply Transformation: Finally, the transformation function is applied to each pixel in the input
image, effectively redistributing the pixel intensities according to the desired distribution.
To equalize the given histogram:
Grey Level - 0 1 2 3 4 5 6 7
No. of Pixels- 790 1023 850 656 329 245 122 81
1. Compute the cumulative distribution function (CDF) from the histogram.

2. Normalize the CDF to the range [0, 1].
3. Multiply each normalized CDF value by the maximum intensity value (in this case, 7) to obtain the
new intensity values for histogram equalization.
4. Apply the transformation to each pixel in the image, replacing the original intensity values with the
corresponding new intensity values.
Once the histogram equalization process is completed, the contrast of the image should be enhanced,
and the distribution of pixel intensities should be more uniform across the intensity range.
solve
Sure, let's solve the histogram equalization for the given histogram:
Grey Level - 0 1 2 3 4 5 6 7
No. of Pixels - 790 1023 850 656 329 245 122 81
1. Compute the Cumulative Distribution Function (CDF):

We first calculate the cumulative sum of the histogram values:
i
CDF(i) = ∑ Histogram(j)
j=0
Then we normalize the CDF to the range [0, 1] by dividing by the total number of pixels in the
image:
CDF(i)
CDFnorm (i) =
Total Pixels

Let's compute it:
Total Pixels = 790 + 1023 + 850 + 656 + 329 + 245 + 122 + 81 = 4096
CDF(0) = 790
CDF(1) = 790 + 1023 = 1813
CDF(2) = 1813 + 850 = 2663
CDF(3) = 2663 + 656 = 3319
CDF(4) = 3319 + 329 = 3648

CDF(5) = 3648 + 245 = 3893

CDF(6) = 3893 + 122 = 4015
CDF(7) = 4015 + 81 = 4096
Now, normalize each of these values:
790
CDFnorm (0) = ≈ 0.192
4096

1813
CDFnorm (1) = ≈ 0.443
4096

2663
CDFnorm (2) = ≈ 0.650
4096

3319
CDFnorm (3) = ≈ 0.810
4096

3648
CDFnorm (4) = ≈ 0.891

4096

3893
CDFnorm (5) = ≈ 0.951
4096

4015
CDFnorm (6) = ≈ 0.980
4096

4096
CDFnorm (7) = = 1.000
4096

2. Compute New Intensity Values:

Now, we need to multiply each normalized CDF value by the maximum intensity value (7) to obtain
the new intensity values for histogram equalization.
New Intensity(i) = CDFnorm (i) × Max Intensity

New Intensity(0) = 0.192 × 7 ≈ 1.344
New Intensity(1) = 0.443 × 7 ≈ 3.101
New Intensity(2) = 0.650 × 7 ≈ 4.550
New Intensity(3) = 0.810 × 7 ≈ 5.670
New Intensity(4) = 0.891 × 7 ≈ 6.237

New Intensity(5) = 0.951 × 7 ≈ 6.657

New Intensity(6) = 0.980 × 7 ≈ 6.860
New Intensity(7) = 1.000 × 7 = 7
3. Apply Transformation to Image:

Replace each pixel's intensity value with its corresponding new intensity value computed above.
After applying the histogram equalization transformation, the image's contrast will be enhanced, and
the distribution of pixel intensities will be more uniform across the intensity range.
Compare and constrast smoothing and sharpening filters.
Explain the process of filtering in spatial domain.
Smoothing and sharpening filters are two types of image filters used in image processing, each serving
different purposes and producing different effects on images. Here's a comparison and contrast
between the two:
Smoothing Filters:
Purpose: Smoothing filters are used to reduce noise and blur in an image by averaging
neighboring pixel values.
Effect: Smoothing filters blur the image by reducing high-frequency components, resulting in a
smoother appearance.
Common Techniques: Gaussian smoothing, mean filtering, and median filtering are common
smoothing techniques.
Application: Smoothing filters are often used in preprocessing to improve the quality of images
before further analysis or to prepare images for tasks such as edge detection or segmentation.
Sharpening Filters:
Purpose: Sharpening filters are used to enhance edges and fine details in an image by increasing
the contrast around edges.
Effect: Sharpening filters enhance high-frequency components in the image, making edges appear
more defined and details more prominent.
Common Techniques: Laplacian sharpening, unsharp masking (USM), and high-pass filtering are
common sharpening techniques.
Application: Sharpening filters are applied to images to improve their visual appearance, making
them appear clearer and more defined. They are often used in post-processing to enhance specific
features or regions of interest in images.
Comparison:
Both smoothing and sharpening filters are applied to images to modify their appearance, but they
have opposite effects on image features.
Smoothing filters reduce noise and blur by averaging neighboring pixel values, while sharpening
filters enhance edges and fine details by increasing contrast.
Smoothing filters result in a smoother appearance of the image, while sharpening filters increase
the apparent sharpness and clarity of the image.
Smoothing filters are used to preprocess images or reduce noise before further analysis, while
sharpening filters are applied to enhance specific features or improve the visual quality of images.
Contrast:
Smoothing filters reduce the contrast between neighboring pixels, resulting in a smoother image
appearance with less distinct edges.
Sharpening filters increase the contrast along edges, making them appear sharper and more
defined.
Now, regarding the process of filtering in the spatial domain:
Filtering in the spatial domain involves applying a filter kernel or mask directly to the pixels of an image
to produce a filtered output. Here's the general process:
1. Define Filter Kernel: A filter kernel is a small matrix or mask that specifies how to weight the
contributions of neighboring pixels when computing the value of a particular pixel in the filtered
image. The size and values of the kernel determine the characteristics of the filter.
2. Convolution: The filter kernel is convolved with the input image by sliding it over each pixel
position in the image. At each position, the kernel is centered on the pixel, and the weighted sum
of the pixel values covered by the kernel is computed.
3. Pixel Value Update: The computed sum is assigned as the new value of the corresponding pixel in
the filtered output image. This process is repeated for every pixel in the input image.
4. Boundary Handling: Depending on the size of the kernel and the position of pixels near the image
boundary, special techniques such as zero-padding, mirror-padding, or periodic padding may be
used to handle boundary effects during convolution.
5. Filtering Effects: The resulting filtered image reflects the effects of the applied filter kernel on the
input image. Different types of filters, such as smoothing, sharpening, edge detection, or noise
reduction filters, produce different effects on the image based on the characteristics of their filter
kernels.
Filtering in the spatial domain is intuitive and computationally efficient, making it widely used in various
image processing applications. However, it may not always be the most effective approach for certain
types of image processing tasks, particularly those involving complex transformations or frequency-
based analysis. In such cases, filtering in the frequency domain using techniques like Fourier transforms
and convolution in the frequency domain may be more appropriate.
Explain thinning and thickening operations with suitable example.
Explain the procedure of region filling with an example.
Thin and thick operations are morphological image processing techniques used to modify the size and
shape of objects in binary images. These operations are particularly useful in tasks such as feature
extraction, shape analysis, and object recognition. Here's an explanation of thinning and thickening
operations with suitable examples:
Thinning Operation:
Thinning is a morphological operation that reduces the thickness of objects in a binary image while
preserving their essential shape and connectivity. It iteratively erodes the boundaries of objects until
they become as thin as possible without breaking apart or losing connectivity.
Example:
Consider a binary image containing a letter "T" as an object. Performing thinning on this image would
gradually reduce the thickness of the letter "T" until it becomes a skeleton-like representation. The
thinning process removes pixels from the object's boundaries in such a way that the essential shape and
connectivity are preserved. After thinning, the resulting image would contain a skeletonized version of
the letter "T" with minimal thickness.
Thickening Operation:
Thickening is the opposite of thinning and is used to increase the thickness of objects in a binary image
while maintaining their original shape and connectivity. It iteratively dilates the boundaries of objects
until they reach the desired thickness.
Example:
Consider a binary image containing a thin line segment representing a road on a map. Performing
thickening on this image would gradually increase the thickness of the road segment without altering its
shape or connectivity. The thickening process adds pixels to the object's boundaries in such a way that
the original shape and connectivity are preserved. After thickening, the resulting image would contain a
road segment with increased thickness while still maintaining its original shape.
In both thinning and thickening operations, the choice of structuring element (kernel) and the number
of iterations can affect the final outcome. These operations are commonly used in various image
processing applications, including pattern recognition, fingerprint analysis, and medical imaging.
Now, let's explain the procedure of region filling with an example:
Region Filling:
Region filling is a process of filling the interior of a region defined by a closed contour in an image. It
involves starting from a seed point within the region and iteratively expanding the filled region until it
encompasses the entire enclosed area.
Procedure:
1. Choose Seed Point: Select a seed point within the region to be filled. This point should be located
within the closed contour of the region.
2. Initialization: Initialize an empty mask or label image with the same dimensions as the input
image. Set the seed point as the starting point for region filling.
3. Iterative Expansion: Starting from the seed point, iteratively expand the filled region by adding
neighboring pixels that meet certain criteria. Common criteria include having similar intensity
values or being connected to already filled pixels.
4. Stop Condition: Continue the iterative expansion until all pixels within the region have been filled,
or until a predefined stopping condition is met. This condition could be reaching the image
boundaries or encountering a pixel with a different intensity value.
5. Output: The final filled region is represented by the pixels labeled or filled during the iterative
expansion process. This region can be visualized or used for further analysis.
Example:
Consider a binary image containing a circular region surrounded by a closed contour. To fill this region,
we start by selecting a seed point within the circular area. We then iteratively expand the filled region by
adding neighboring pixels that are part of the circle until the entire enclosed area is filled. The result is a
filled region representing the interior of the circle, and the boundary of the circle remains unchanged.
Region filling is commonly used in various image processing tasks such as image segmentation, object
recognition, and image editing, where it helps in isolating and analyzing specific regions of interest
within an image.
Explain stereo imaging and multi-level thresholding.
Explain edge detection and edge linking. Also write the difference between them.
Certainly! Let's start with explanations for stereo imaging and multi-level thresholding:
Stereo Imaging:
Stereo imaging involves capturing and processing images from two or more cameras positioned slightly
apart from each other to create a perception of depth. The human visual system uses a similar principle,
where the brain combines the slightly different views from our two eyes to perceive depth and three-
dimensional (3D) structure. Stereo imaging systems replicate this process using cameras to capture
images from different viewpoints.
Here's how stereo imaging works:
1. Image Acquisition: Two or more cameras capture images of the same scene from slightly
different perspectives.
2. Image Rectification: The captured images are rectified to ensure that corresponding points in the
images line up along the same scanlines. This step is crucial for accurate stereo matching.
3. Stereo Matching: Corresponding points in the rectified images are matched to determine
disparities (horizontal offsets) between them. These disparities are inversely proportional to the
depth of objects in the scene.
4. Depth Estimation: Using the calculated disparities and known camera parameters, the depth of
objects in the scene is estimated using triangulation or similar techniques.
5. 3D Reconstruction: The depth information is used to generate a 3D model of the scene, allowing
for visualization of depth and perspective.
Stereo imaging finds applications in various fields such as robotics, computer vision, augmented reality,
and medical imaging.
Multi-level Thresholding:
Multi-level thresholding is a technique used in image segmentation to partition an image into multiple
regions based on pixel intensity values. Instead of a single threshold, multiple threshold values are used
to segment the image into several intensity-based classes.
Here's how multi-level thresholding works:
1. Histogram Analysis: The histogram of the image is analyzed to identify multiple peaks or modes
corresponding to different intensity levels in the image.
2. Threshold Selection: Threshold values are selected to separate the histogram into distinct regions
corresponding to different intensity classes. This can be done manually or using automated
techniques such as Otsu's method or entropy-based thresholding.
3. Segmentation: The image is segmented into multiple regions based on the selected threshold
values, with pixels assigned to different classes according to their intensity levels.
Multi-level thresholding is effective for segmenting images with complex intensity distributions or
containing multiple objects with different intensity characteristics.
Now, let's move on to edge detection and edge linking, along with their differences:
Edge Detection:
Edge detection is a fundamental technique in image processing used to identify abrupt changes in pixel
intensity that correspond to object boundaries or transitions between different regions in an image.
Edges represent significant local variations in image intensity and are crucial for tasks such as object
detection, shape analysis, and image segmentation.
Common edge detection techniques include:
Gradient-based methods: Detect edges by identifying regions of high gradient magnitude in the
image.
Laplacian of Gaussian (LoG): Combines Gaussian smoothing with Laplacian filtering to detect
edges at multiple scales.
Canny edge detector: Multi-stage algorithm involving Gaussian smoothing, gradient
computation, non-maximum suppression, and edge tracking by hysteresis.
Edge Linking:
Edge linking, also known as edge tracing or edge following, is the process of connecting individual edge
points detected by an edge detector to form continuous curves or contours representing object
boundaries in an image. Edge linking is essential for extracting meaningful shapes and structures from
edge maps generated by edge detection algorithms.
Common edge linking techniques include:
Hough transform: Detects straight lines or curves in an image by converting edge points into
parametric representations (e.g., lines in Hough space).
Region-based methods: Group neighboring edge points into coherent regions or contours based
on criteria such as proximity, similarity, or continuity.
Dynamic programming: Finds the optimal path along connected edge points by minimizing a cost
function based on edge strength and smoothness.
Differences:
Purpose: Edge detection aims to identify individual edge points or pixels in an image, whereas
edge linking focuses on connecting these edge points to form continuous curves or contours
representing object boundaries.
Output: Edge detection produces a binary or grayscale edge map highlighting regions of high
gradient magnitude, while edge linking generates connected curves or contours representing
object boundaries.
Processing: Edge detection typically involves local operations such as gradient computation and
thresholding, while edge linking requires more global processing to connect edge points into
meaningful structures.
Applications: Edge detection is used for tasks such as feature extraction, image segmentation,
and object recognition, while edge linking is essential for shape analysis, object tracking, and
boundary extraction.
What is image restoration? Draw and explain the basic block diagram of the restoration process.
Give two areas where the restoration process can be applied.
Write a short note on deterministic methods of object recognition.
Image restoration refers to the process of improving the quality of an image by removing or reducing
degradations that occurred during image acquisition, transmission, or storage. Degradations can
include blur, noise, compression artifacts, and other forms of distortion. The goal of image restoration is
to recover the original, undegraded image as accurately as possible.
Basic Block Diagram of Image Restoration Process:
1. Observation Model: The process begins with the observation of a degraded image, which is
modeled as the result of applying a degradation process (e.g., blur, noise) to the original image.
The observation model describes how the degraded image is related to the original image and the
degradation process.
2. Degradation Model: The degradation model represents the effects of various factors that cause
image degradation. This includes mathematical models for blur, noise, and other distortions. The
degradation model characterizes how the original image is transformed into the degraded image.
3. Restoration Algorithm: The restoration algorithm is responsible for estimating the original image
from the degraded observation. It involves applying inverse operations to reverse the effects of the
degradation process and recover the original image as closely as possible.
4. Performance Evaluation: The quality of the restored image is evaluated using appropriate metrics
such as peak signal-to-noise ratio (PSNR), structural similarity index (SSI), or visual inspection. This
step assesses how effectively the restoration algorithm has recovered the original image.
Areas of Application for Image Restoration:
1. Medical Imaging: In medical imaging, image restoration techniques are used to improve the
quality and clarity of medical images such as MRI, CT scans, and ultrasound images. Restoration
helps enhance diagnostic accuracy and enables better visualization of anatomical structures.
2. Remote Sensing: In remote sensing applications such as satellite imaging and aerial photography,
image restoration is used to remove atmospheric effects, sensor noise, and other distortions
introduced during image acquisition. This improves the accuracy of remote sensing data for
environmental monitoring, land use classification, and urban planning.
Note on Deterministic Methods of Object Recognition:
Deterministic methods of object recognition refer to approaches that rely on explicit rules, algorithms,
or mathematical models to recognize objects in images. These methods involve precise computational
techniques for analyzing image features and making decisions based on predefined criteria. Some
common deterministic methods of object recognition include:
1. Feature-Based Matching: Detecting and matching key features or descriptors in images to

identify objects. Features can include points, edges, corners, or texture patterns. Matching is often
performed using techniques such as template matching, corner detection, or scale-invariant
feature transform (SIFT).
2. Geometric Models: Representing objects using geometric models such as lines, circles, ellipses, or
polygons, and matching these models to image data. Geometric models can capture object shape,
size, and spatial relationships, enabling robust object recognition under various conditions.
3. Statistical Classifiers: Using statistical classifiers such as support vector machines (SVM), neural
networks, or Bayesian classifiers to classify objects based on extracted image features. These
classifiers learn from labeled training data and can discriminate between different object classes
based on their statistical properties.
Deterministic methods of object recognition offer advantages such as explicit control over recognition
criteria, predictable performance, and interpretability of results. However, they may be sensitive to
variations in imaging conditions, object appearance, and scene complexity, requiring careful design and
tuning of algorithms for optimal performance.
draw block daigram using ascii art
Sure, here's a basic block diagram using ASCII art representing the image restoration process:
lua
+---------------------+ +----------------------+ +---------------------+

| | | | | |
| Degraded Observation| ---> | Degradation Model | ---> | |
| (Input) | | | | Restoration |
| | | | | Algorithm |
+---------------------+ +----------------------+ +---------------------+
| | |
| Observation | Estimated |
| Model | Original |
| | Image |
| | |
| +---------------------+ |
| | |
+-------------------------------------------------+ |
|
Performance Evaluation |
(Quality Metrics) |
|
+-----------------------------------------------------+
|
v
Restored Image
(Output)
In this diagram:
The "Degraded Observation" represents the input image that has undergone degradation during
acquisition, transmission, or storage.
The "Degradation Model" describes the degradation process and how it affects the original image
to produce the degraded observation.
The "Restoration Algorithm" estimates the original image from the degraded observation using
inverse operations to reverse the degradation process.
The "Performance Evaluation" assesses the quality of the restored image using metrics such as
PSNR, SSI, or visual inspection.
The "Restored Image" is the output of the restoration process, representing the recovered original
image as closely as possible.
Each block in the diagram represents a stage or component of the image restoration process, and
arrows indicate the flow of information between them.
Describe the geometric mean filter.
Define the following terms: N4 (p),N8 (p) & Nd (p)
What do mean by color model?
Write the equation for Gaussian low pass filter.
e) What is convex hull?
Define image segmentation.
Define Histogram.
What is clustering?
Compare the basic frequency domain filters Ideal low pass, Butterworth low pass and Gaussian low
pass.
Define edge detection and edge linking. Also write difference between them.
Explain Dilation and erosion
For a 3 bit 4*4 size image perform
(1)Negation
(ii)Thresholding with T-4
(iii) Bit plane slicing
Explain the components of digital image processing along with the suitable block diagram.
Equalize the below histogram
Gray level - 0 1 2 3 4 5 6 7
nk - 100 90 50 20 0 0 0 0
Define image enhancement and discuss the point operations in image enhancement?
Explain about Homomorphic filtering with necessary equations
Outline the importance of Geometric mean filter.
Explain thinning and thickening operators.
Explain stereo imaging.
Write a short note on morphological algorithms
Explain multi-level thresholding.
Write a short not on tree-search.
Write short note on region-based segmentation method.
Answer all the above questions and sub questions in detail with explanation
That's a comprehensive list of questions covering various aspects of image processing and related
topics. I'll provide detailed explanations for each question and sub-question.
1. Geometric Mean Filter:

The geometric mean filter is a type of spatial domain filtering used in image processing to perform
noise reduction. It replaces each pixel value in the image with the geometric mean of the pixel
values in its neighborhood. This filter is particularly effective in removing multiplicative noise, such
as speckle noise in medical images or grain noise in digital photographs.
2. Definition of Terms:
N4 (p): N4 neighborhood of pixel p includes the pixel p itself and the pixels immediately
north, south, east, and west of p.
north, south, east, west, northeast, northwest, southeast, and southwest of p.
Nd (p): Nd neighborhood of pixel p includes the pixel p itself and all pixels within a distance d
of p in any direction, where d is a predefined distance.
3. Color Model:
A color model is a mathematical model used to represent colors as tuples of numbers. It defines
how colors are represented and manipulated in digital images. Common color models include RGB
(Red, Green, Blue), CMYK (Cyan, Magenta, Yellow, Black), HSL (Hue, Saturation, Lightness), and
YCbCr (Luminance, Chrominance Blue, Chrominance Red).
4. Equation for Gaussian Low Pass Filter:
The equation for a Gaussian low-pass filter in the spatial domain is given by:
D(u,v)2
−
H(u, v) = e 2σ 2

Where:
H(u, v) is the filter transfer function.

D(u, v) is the distance from the origin in the frequency domain.
σ is the standard deviation of the Gaussian distribution, controlling the amount of smoothing.
5. Convex Hull:
A convex hull of a set of points in a Euclidean space is the smallest convex polygon that contains all
the points in the set. It represents the outer boundary of the points, ensuring that any line
segment connecting two points inside the polygon lies entirely within the polygon.
6. Image Segmentation:
Image segmentation is the process of partitioning an image into multiple segments or regions
based on certain characteristics such as color, intensity, texture, or motion. The goal is to simplify
and/or change the representation of an image into something that is more meaningful and easier
to analyze.
7. Histogram:
A histogram is a graphical representation of the distribution of pixel intensity values in an image. It
plots the frequency of occurrence of each intensity value (or intensity range) along the horizontal
axis, while the vertical axis represents the number of pixels with that intensity value.
8. Clustering:
Clustering is a process of grouping similar objects or data points together based on certain criteria.
In image processing, clustering techniques are often used for tasks such as image segmentation,
where pixels with similar characteristics are grouped into clusters representing distinct regions or
objects in the image.
9. Filtering in Spatial Domain:

Filtering in the spatial domain involves applying a filter directly to the pixels of an image in the
spatial (or spatial-temporal) domain. It operates on the raw pixel values of the image without any
transformation to frequency space. Techniques such as convolution and morphological operations
are commonly used for spatial domain filtering.
10. Comparison of Frequency Domain Filters:
Ideal Low Pass Filter: Ideal low-pass filters completely preserve low-frequency components
while completely eliminating high-frequency components. However, it results in severe
ringing artifacts and requires an infinite support.
Butterworth Low Pass Filter: Butterworth low-pass filters provide a smooth transition
between passband and stopband with a controlled roll-off rate. They offer better
performance than ideal filters but may still exhibit some ripple in the passband.
Gaussian Low Pass Filter: Gaussian low-pass filters have a Gaussian-shaped frequency
response, offering smooth attenuation of high frequencies with no ringing artifacts. They
provide a good compromise between sharpness and smoothness.
11. Edge Detection and Edge Linking:
Edge Detection: Edge detection involves detecting sudden changes in pixel intensity, which
typically indicate object boundaries or significant transitions in an image. Common edge
detection techniques include Sobel, Prewitt, and Canny edge detectors.
Edge Linking: Edge linking, also known as edge tracing or edge following, is the process of
connecting individual edge points detected by an edge detector to form continuous curves or
contours representing object boundaries in an image.
12. Dilation and Erosion:
Dilation: Dilation is a morphological operation that expands or thickens the boundaries of

objects in a binary image. It involves sliding a structuring element over the image and setting
the center pixel of the structuring element to the maximum value of the pixels it covers.
Erosion: Erosion is a morphological operation that shrinks or thins the boundaries of objects
in a binary image. It involves sliding a structuring element over the image and setting the
center pixel of the structuring element to the minimum value of the pixels it covers.
13. Image Processing Operations for a 3-bit 4x4 Image:
Negation: Negation involves computing the complement of each pixel value, i.e., subtracting
each pixel value from the maximum intensity value. For a 3-bit image, the maximum intensity
value is 7, so negation would involve subtracting each pixel value from 7.
Thresholding with T-4: Thresholding with T-4 involves setting pixel values below the
threshold value of 4 to 0 and pixel values above or equal to the threshold to 1.
Bit Plane Slicing: Bit plane slicing involves extracting individual bit planes from the image,
representing different levels of intensity. For a 3-bit image, there would be 3 bit planes
representing the most significant bit (MSB), middle bit, and least significant bit (LSB).
14. Components of Digital Image Processing:

Digital image processing involves several components:
Image Acquisition: Capturing images using cameras, scanners, or other imaging devices.
Preprocessing: Enhancing image quality through operations like noise reduction, contrast
enhancement, and geometric correction.
Image Restoration: Removing or reducing degradations in images caused by factors like
blur, noise, or compression.
continue
markdown
- **Image Enhancement:** Improving the visual appearance of images through operations such
as sharpening, contrast stretching, and color adjustment.
- **Image Analysis:** Extracting meaningful information from images through tasks like
object detection, feature extraction, and pattern recognition.
- **Image Interpretation:** Interpreting the extracted information for decision making or
further analysis.
15. Equalization of Histogram:

Histogram equalization is a technique used to enhance the contrast of an image by redistributing pixel
intensities. It aims to make the histogram of the image as uniform as possible. The process involves
mapping the original pixel intensities to new values such that the cumulative distribution function (CDF)
of the resulting histogram is approximately linear.
vbnet
For the given histogram:

```
Grey Level - 0 1 2 3 4 5 6 7
No. of Pixels - 100 90 50 20 0 0 0 0
```
To equalize the histogram, you would:

- Compute the cumulative histogram.
- Normalize the cumulative histogram to obtain the transformation function.
- Apply the transformation function to each pixel in the image.
16. Image Enhancement and Point Operations:

Image enhancement is the process of improving the visual appearance of an image to make it more
suitable for a specific application or to better meet human perception. Point operations in image
enhancement involve modifying individual pixel values based on mathematical operations or predefined
functions. Examples include contrast stretching, histogram equalization, and gamma correction.
17. Homomorphic Filtering:

Homomorphic filtering is a technique used to enhance the contrast and brightness of images by
simultaneously adjusting the illumination and reflectance components. It is particularly useful for
images affected by non-uniform illumination, such as those captured under varying lighting
conditions. The basic equation for homomorphic filtering is:
g(x, y) = log(1 + γ ⋅ ∣F (u, v)∣)
Where:
g(x, y) is the filtered image.

F (u, v) is the Fourier transform of the input image.
γ is a parameter controlling the degree of enhancement.
18. Importance of Geometric Mean Filter:

The geometric mean filter is important in image processing for its ability to effectively reduce
multiplicative noise while preserving image details. It is particularly useful in applications where
images are corrupted by noise with a multiplicative nature, such as medical imaging and remote
sensing. The filter provides a balance between noise reduction and preservation of image
sharpness, making it a valuable tool in image restoration and enhancement.
19. Thinning and Thickening Operators:
Thinning: Thinning operators are used to reduce the thickness of objects in binary images
while preserving their connectivity and shape. They iteratively erode the boundaries of
objects until they become as thin as possible without breaking apart.
Thickening: Thickening operators, on the other hand, are used to increase the thickness of
objects in binary images while maintaining their shape and connectivity. They iteratively dilate
the boundaries of objects until they reach the desired thickness.
20. Stereo Imaging:

Stereo imaging involves capturing and processing images from two or more cameras positioned
slightly apart to create a perception of depth. It replicates the human visual system's ability to
perceive depth by combining slightly different views from multiple viewpoints. Stereo imaging
finds applications in fields such as robotics, computer vision, and virtual reality.
21. Morphological Algorithms:

Morphological algorithms are a class of image processing techniques based on mathematical
morphology. They operate on the shape and structure of objects in images and are particularly
useful for tasks such as noise removal, edge detection, and shape analysis. Common
morphological operations include dilation, erosion, opening, closing, and thinning.
22. Multi-level Thresholding:

Multi-level thresholding is a technique used in image segmentation to partition an image into
multiple regions based on pixel intensity values. Instead of a single threshold, multiple threshold
values are used to segment the image into several intensity-based classes. It is effective for
segmenting images with complex intensity distributions or containing multiple objects with
different intensity characteristics.
23. Tree-Search:
Tree-search refers to a class of algorithms that explore a search space represented as a tree data
structure. It involves systematically traversing the tree to find a solution to a problem or to perform
a specific task. Tree-search algorithms are widely used in various fields, including artificial
intelligence, optimization, and computer science.
24. Region-Based Segmentation:

Region-based segmentation is a method of partitioning an image into regions or segments based
on certain criteria such as color, intensity, texture, or motion. It involves grouping pixels into
homogeneous regions with similar characteristics and boundaries. Region-based segmentation
techniques include region growing, split and merge, and watershed segmentation.
These explanations cover a wide range of topics in image processing and related areas. If you have any
further questions or need more details on any topic, feel free to ask!
Describe the geometric mean filter.
The geometric mean filter is a type of spatial domain filtering technique used in image processing for
noise reduction, particularly for multiplicative noise. It is designed to preserve image details while
effectively reducing noise, making it particularly useful in applications such as medical imaging and
remote sensing where images are often corrupted by multiplicative noise sources like speckle noise or
grain noise.
Here's how the geometric mean filter works:
1. Local Neighborhood Operation:

Like other spatial domain filters, the geometric mean filter operates on each pixel of the image by
considering a local neighborhood around it. The size of the neighborhood, typically defined by a
square or rectangular window, determines the extent of the filtering operation.
2. Pixel Value Calculation:

For each pixel in the image, the geometric mean filter computes the geometric mean of the pixel
values within the local neighborhood. The geometric mean is calculated by taking the nth root of
the product of the pixel values, where n is the number of pixels in the neighborhood.
3. Noise Reduction:
The geometric mean filter is effective in reducing multiplicative noise because it calculates the
mean in a multiplicative scale rather than an additive one. Multiplicative noise tends to corrupt
image details by amplifying or attenuating pixel values, and taking the geometric mean helps
mitigate the effects of such noise.
4. Preservation of Image Details:

Unlike some other smoothing filters, the geometric mean filter tends to preserve image details and
edges while reducing noise. This is because the geometric mean of pixel values within a local
neighborhood gives less weight to extreme values compared to the arithmetic mean, resulting in
smoother transitions between neighboring pixels.
5. Implementation Considerations:
When implementing the geometric mean filter, it's important to consider the size of the
neighborhood window. Larger windows provide better noise reduction but may blur image details,
while smaller windows preserve more details but may be less effective in reducing noise. Choosing
an appropriate window size depends on the characteristics of the noise and the desired trade-off
between noise reduction and detail preservation.
Overall, the geometric mean filter is a valuable tool in image processing for reducing multiplicative noise
while preserving image details, making it suitable for a wide range of applications where noise reduction
is critical without sacrificing important visual information.
Define the following terms: N4 (p),N8 (p) & Nd (p)
What do mean by color model?
Certainly! Let's define the terms and then discuss the concept of a color model:
1. N4 (p), N8 (p), and Nd (p):
north, south, east, and west of p. It represents the 4-connected neighborhood of pixel p in a
2D image.
north, south, east, west, northeast, northwest, southeast, and southwest of p. It represents
the 8-connected neighborhood of pixel p in a 2D image.
Nd (p): Nd neighborhood of pixel p includes the pixel p itself and all pixels within a distance d
of p in any direction, where d is a predefined distance. It represents the neighborhood of
pixel p in a distance-based metric, allowing for more flexible neighborhood definitions
beyond the 4 or 8 connectivity.
2. Color Model:
A color model is a mathematical representation used to describe and define colors in a way that
can be interpreted and manipulated by computers. It specifies how colors are represented
numerically and how different color attributes such as hue, saturation, and intensity are encoded.
Color models are essential for storing, processing, and displaying color images in digital systems.
Commonly used color models include:
RGB (Red, Green, Blue): Represents colors as combinations of red, green, and blue primary
colors. Each color component is typically represented by an 8-bit value ranging from 0 to 255,
allowing for a wide range of colors to be expressed.
CMYK (Cyan, Magenta, Yellow, Black): Used primarily in color printing, CMYK represents
colors as combinations of cyan, magenta, yellow, and black inks. It is a subtractive color
model, where colors are created by subtracting certain wavelengths of light.
HSV/HSL (Hue, Saturation, Value/Lightness): These models represent colors based on their
hue (the dominant wavelength), saturation (the intensity or purity of the color), and
value/lightness (the brightness or darkness of the color). They are often used in graphics
software for intuitive color manipulation.
Each color model has its advantages and is suitable for different applications. RGB is widely used in
digital displays and image processing, while CMYK is common in printing. HSV/HSL models are
popular for color selection and adjustment tasks due to their intuitive representation of color
attributes.
Write the equation for Gaussian low pass filter.
e) What is convex hull?
The equation for a Gaussian low-pass filter in the spatial domain is given by:
D(u,v)2
H(u, v) = e− 2σ 2

Where:
H(u, v) is the filter transfer function.

D(u, v) is the distance from the origin in the frequency domain.
σ is the standard deviation of the Gaussian distribution, controlling the amount of smoothing.
Now, regarding the concept of a convex hull:
Convex Hull:
In geometry, the convex hull of a set of points in a Euclidean space is defined as the smallest convex
polygon that contains all the points in the set. Geometrically, it represents the outer boundary or
envelope of the set of points, such that any line segment connecting two points inside the polygon lies
entirely within the polygon.
Mathematically, the convex hull of a set of points P can be defined as the intersection of all convex sets
that contain P . In 2D space, the convex hull is a convex polygon formed by the vertices of the outermost
boundary of the set of points.
Convex hulls are commonly used in computational geometry and image processing for various tasks
such as shape analysis, object recognition, collision detection, and computational geometry algorithms.
They provide a compact representation of the spatial arrangement of points and are particularly useful
for analyzing and describing the geometry of objects in images or geometric datasets.
Define image segmentation.
Define Histogram.
Certainly!
Image Segmentation:
Image segmentation is a fundamental task in image processing that involves partitioning an image into
multiple meaningful regions or segments. The goal of segmentation is to simplify the representation of
an image by grouping pixels with similar characteristics, such as color, intensity, texture, or motion, into
coherent regions. Each segment typically corresponds to a distinct object or region of interest within the
image.
Image segmentation plays a crucial role in various computer vision and image analysis applications,
including object detection and recognition, medical image analysis, scene understanding, and robotics.
It enables higher-level analysis and interpretation of images by providing a more structured
representation of image content.
There are different approaches to image segmentation, including thresholding, region-based

segmentation, edge-based segmentation, and clustering-based segmentation. Each method has its
advantages and limitations, and the choice of segmentation technique depends on factors such as
image characteristics, application requirements, and computational complexity.
Histogram:
A histogram is a graphical representation of the distribution of pixel intensity values in an image. It plots
the frequency of occurrence of each intensity value (or intensity range) along the horizontal axis, while
the vertical axis represents the number of pixels with that intensity value.
Histograms are widely used in image processing for various tasks, including image enhancement,
contrast adjustment, and image analysis. They provide valuable insights into the distribution of pixel
intensities within an image, helping to understand its overall brightness, contrast, and dynamic range.
In a grayscale image, the histogram typically consists of bins representing discrete intensity values
ranging from 0 (black) to 255 (white) for an 8-bit image. Each bin corresponds to a range of intensity
values, and the height of the bin indicates the number of pixels with intensity values falling within that
range.
Histogram equalization, histogram matching, and histogram specification are common techniques that
leverage histograms for image enhancement and contrast adjustment. In color images, separate
histograms are computed for each color channel (e.g., red, green, blue) or color space (e.g., RGB, HSV),
providing insights into the distribution of color intensities across different channels.
What is clustering?
Certainly!
Clustering:
Clustering is a technique used in data analysis and pattern recognition to group similar data points or
objects together based on their characteristics. In the context of image processing, clustering involves
grouping similar pixels or regions in an image based on certain features such as color, intensity, texture,
or spatial proximity.
The goal of clustering is to partition the image into clusters or segments such that pixels within the
same cluster are more similar to each other than to those in other clusters. Clustering algorithms aim to
maximize intra-cluster similarity and minimize inter-cluster similarity, often using distance metrics such
as Euclidean distance or Mahalanobis distance to measure similarity between pixels.
Clustering techniques are widely used in image processing for tasks such as image segmentation, object
recognition, and feature extraction. Common clustering algorithms include K-means clustering,
hierarchical clustering, and density-based clustering methods like DBSCAN.
Process of Filtering in Spatial Domain:

Filtering in the spatial domain involves applying a filter directly to the pixels of an image in the spatial (or
spatial-temporal) domain, without transforming the image to the frequency domain. Spatial domain
filters operate on the raw pixel values of the image, typically using convolution operations to process
each pixel and its neighbors.
The process of filtering in the spatial domain can be summarized as follows:
1. Define Filter Kernel:

Choose or design a filter kernel, which is a small matrix or mask representing the weights of the
filter. The size and shape of the kernel depend on the specific filtering operation and the desired
characteristics of the filter.
2. Convolution Operation:
Slide the filter kernel over each pixel in the image, computing the weighted sum of pixel values
within the neighborhood defined by the kernel. This operation is known as convolution, and it
produces a new pixel value for each location in the output image based on the filtered values of
the input pixels.
3. Boundary Handling:
Handle boundary conditions, such as edge pixels where the filter kernel extends beyond the image
boundaries. Various techniques can be used for boundary handling, including zero-padding,
mirror-padding, or periodic boundary extension.
4. Output Image:
Generate the output image by applying the convolution operation to each pixel in the input image.
The resulting image represents the filtered version of the original image, with desired spatial
modifications such as smoothing, sharpening, or edge detection.
Spatial domain filtering techniques include linear filters such as Gaussian blur, median filter, and Sobel
edge detector, as well as nonlinear filters such as morphological filters and bilateral filter. These filters
play a crucial role in various image processing tasks, including noise reduction, feature extraction, and
image enhancement.
Compare the basic frequency domain filters Ideal low pass, Butterworth low pass and Gaussian low
pass.
Define edge detection and edge linking. Also write difference between them.
Let's compare the basic frequency domain filters Ideal low pass, Butterworth low pass, and Gaussian low
pass, and then discuss edge detection and edge linking along with their differences:
Comparison of Basic Frequency Domain Filters:
1. Ideal Low Pass Filter:
Characteristics: Ideal low-pass filters have a sharp cutoff frequency beyond which all
frequencies are completely attenuated.
Frequency Response: In the frequency domain, the ideal low-pass filter has a rectangular
frequency response, with perfect passband transmission up to the cutoff frequency and zero
transmission beyond.
Advantages: Simple and easy to implement, provides a sharp cutoff.
Disadvantages: Prone to ringing artifacts (Gibbs phenomenon) and requires an infinitely
large filter kernel in the spatial domain.
2. Butterworth Low Pass Filter:
Characteristics: Butterworth low-pass filters have a smoother frequency response compared

to ideal filters, with a gradual transition between passband and stopband.
Frequency Response: The frequency response of a Butterworth low-pass filter decreases
monotonically from unity (passband) to zero (stopband) with a controlled roll-off rate.
Advantages: Provides a smoother transition between passband and stopband, adjustable
roll-off rate.
Disadvantages: More computationally complex compared to ideal filters, may exhibit ripple
in the passband.
3. Gaussian Low Pass Filter:
Characteristics: Gaussian low-pass filters have a Gaussian-shaped frequency response,

providing smooth attenuation of high frequencies.
Frequency Response: The frequency response of a Gaussian low-pass filter follows a
Gaussian distribution, with rapid attenuation of high frequencies and no sharp cutoff.
Advantages: Offers smooth frequency attenuation, no ringing artifacts, and adjustable
smoothing.
Disadvantages: May not provide as sharp a cutoff as ideal filters, requires parameter tuning
for the standard deviation.
Definition and Difference between Edge Detection and Edge Linking:
Edge Detection:
Edge detection is a fundamental image processing technique aimed at identifying the boundaries or
transitions between different regions in an image. The edges represent significant changes in intensity,
texture, or color, often indicating object boundaries or discontinuities in the image content.
Edge Linking:
Edge linking, also known as edge tracing or edge following, is the process of connecting individual edge
points detected by an edge detector to form continuous curves or contours representing object
boundaries in an image. It involves grouping adjacent edge points that belong to the same edge and
linking them together to create a coherent representation of the edge structure.
Difference between Edge Detection and Edge Linking:

The main differences between edge detection and edge linking are as follows:
1. Objective:
Edge detection aims to identify and localize points in the image where significant intensity
changes occur, highlighting potential object boundaries or features.
Edge linking focuses on connecting these individual edge points to form continuous curves or
contours, reconstructing the shape of objects or regions in the image.
2. Output:
Edge detection typically produces a binary image or a map of edge pixels, where each pixel is
labeled as an edge point or a non-edge point based on certain criteria.
Edge linking generates connected curves or contours representing the boundaries of objects
or regions in the image, providing a more coherent representation of the image structure.
3. Processing Steps:
Edge detection involves applying a specific edge detection algorithm (e.g., Sobel, Canny) to
the input image to detect edge points.
Edge linking follows edge detection and involves post-processing steps to connect adjacent
edge points and form continuous curves or contours.
4. Purpose:
Edge detection is often used as a preprocessing step for tasks such as object detection, shape
analysis, and feature extraction.
Edge linking is essential for tasks such as object boundary delineation, image segmentation,
and pattern recognition, where the precise shape and structure of objects are required.
Explain Dilation and erosion
For a 3 bit 4*4 size image perform
(1)Negation
(ii)Thresholding with T-4
(iii) Bit plane slicing
Certainly! Let's start by explaining dilation and erosion, and then we'll perform the given operations on
the 3-bit 4x4 size image.
Explanation of Dilation and Erosion:
Dilation:
Dilation is a morphological operation used to enhance or thicken the boundaries of objects in a binary
image. It works by sliding a structuring element (also known as a kernel or mask) over the image and
setting the center pixel of the structuring element to the maximum value of the pixels it covers. Dilation
expands the shapes and fills in small gaps or holes in the objects, making them more prominent.
Erosion:
Erosion is a morphological operation used to erode or thin the boundaries of objects in a binary image.
It works by sliding a structuring element over the image and setting the center pixel of the structuring
element to the minimum value of the pixels it covers. Erosion shrinks the shapes and removes small
protrusions or irregularities from the objects, making them thinner.
Now, let's perform the given operations on the 3-bit 4x4 size image:
3-bit 4x4 Size Image:
1 0 1 1
0 1 0 1
1 1 1 0
0 0 1 0
Performing Operations:
(1) Negation:
Negation involves computing the complement of each pixel value, i.e., subtracting each pixel value from
the maximum intensity value. For a 3-bit image, the maximum intensity value is 7. Therefore, the
negation operation would result in:
6 7 6 6
7 6 7 6
6 6 6 7
7 7 6 7
(ii) Thresholding with T-4:

Thresholding with T-4 involves setting pixel values below the threshold value of 4 to 0 and pixel values
above or equal to the threshold to 1.
1 0 1 1
0 1 0 1
1 1 1 0
0 0 1 0
Since all pixel values in the given image are already below the threshold value of 4, there is no change in
the image after thresholding.
(iii) Bit Plane Slicing:

Bit plane slicing involves extracting individual bit planes from the image, representing different levels of
intensity. For a 3-bit image, there would be 3 bit planes representing the most significant bit (MSB),
middle bit, and least significant bit (LSB).
yaml
MSB:
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
Middle Bit:
1 1 1 1
0 0 0 0
1 1 1 0
0 0 0 0
LSB:
1 0 1 1
0 1 0 1
1 1 1 0
0 0 1 0
These are the results after performing the given operations on the 3-bit 4x4 size image. Let me know if
you need further clarification on any part!
Explain the components of digital image processing along with the suitable block diagram.
Equalize the below histogram
Gray level - 0 1 2 3 4 5 6 7
nk - 100 90 50 20 0 0 0 0
Sure! Let's start by explaining the components of digital image processing along with a suitable block
diagram, and then we'll proceed to equalizing the given histogram.
Components of Digital Image Processing:
Digital image processing involves various components or stages that collectively contribute to the
analysis, manipulation, and interpretation of digital images. These components can be broadly
categorized into the following stages:
1. Image Acquisition:
This stage involves capturing or acquiring images from various sources such as cameras, scanners,
or medical imaging devices. The acquired images may be in the form of grayscale or color images
and can vary in resolution and quality.
2. Preprocessing:
Preprocessing is the initial stage of image processing where the acquired images are subjected to
preprocessing operations to enhance their quality and suitability for further analysis. This stage
may include operations such as noise reduction, image denoising, contrast enhancement, and
image normalization.
3. Image Enhancement:
Image enhancement techniques are applied to improve the visual quality or appearance of images
by emphasizing certain features or reducing unwanted artifacts. This stage may involve operations
such as histogram equalization, contrast stretching, sharpening, and color correction.
4. Image Restoration:
Image restoration techniques are used to recover or restore degraded images by compensating for
various types of distortions or degradation processes. This stage may include operations such as
image deblurring, image inpainting, and image super-resolution.
5. Image Analysis:
Image analysis involves extracting meaningful information or features from images for further
interpretation or decision-making. This stage may include tasks such as object detection, image
segmentation, feature extraction, and pattern recognition.
6. Image Interpretation:
Image interpretation is the final stage of image processing where the extracted information or
features are interpreted to derive insights, make decisions, or perform specific tasks based on the
analyzed image data. This stage may involve tasks such as object classification, scene
understanding, and image-based decision-making.
Block Diagram of Digital Image Processing:
Below is a simplified block diagram illustrating the components of digital image processing:
mathematica
Image Acquisition
|
v
Preprocessing
|
v
Image Enhancement
|
v
Image Restoration
|
v
Image Analysis
|
v
Image Interpretation
This block diagram illustrates the sequential flow of operations involved in digital image processing,
starting from image acquisition and proceeding through preprocessing, enhancement, restoration,
analysis, and interpretation stages.
Now, let's proceed to equalize the given histogram.
Equalization of the Given Histogram:
Histogram Equalization is a technique used to enhance the contrast of an image by redistributing pixel
intensities. It aims to make the histogram of the image as uniform as possible.
Given histogram:
mathematica
Gray Level - 0 1 2 3 4 5 6 7
nk - 100 90 50 20 0 0 0 0
To equalize the histogram, we need to perform the following steps:
1. Compute the cumulative histogram (CDF) from the given histogram.

2. Normalize the cumulative histogram to obtain the transformation function.
3. Apply the transformation function to each pixel in the image.
Let's calculate the cumulative histogram and equalize the given histogram:
mathematica
Cumulative Histogram (CDF):

Gray Level - 0 1 2 3 4 5 6 7
CDF - 100 190 240 260 260 260 260 260
Normalization:
Gray Level - 0 1 2 3 4 5 6 7
CDF - 0 1 2 3 3 3 3 3
Equalized Histogram:
Gray Level - 0 1 2 3 4 5 6 7
nk_eq - 100 90 50 20 0 0 0 0
The equalized histogram shows the distribution of pixel intensities after histogram equalization. Each
pixel value in the original image is replaced with its corresponding value from the equalized histogram,
resulting in an image with improved contrast.
solve step by step
Sure, let's solve the histogram equalization step by step:
Step 1: Compute the Cumulative Histogram (CDF):
The cumulative histogram (CDF) is obtained by summing up the histogram values from the first bin to
the current bin.
mathematica
Gray Level - 0 1 2 3 4 5 6 7
nk - 100 90 50 20 0 0 0 0
CDF:
Gray Level - 0 1 2 3 4 5 6 7
CDF - 100 190 240 260 260 260 260 260
The CDF at each gray level represents the cumulative number of pixels up to that gray level.
Step 2: Normalize the Cumulative Histogram:
Normalize the cumulative histogram values to be in the range [0, L-1], where L is the number of gray
levels (in this case, L = 8).
mathematica
Gray Level - 0 1 2 3 4 5 6 7
CDF - 0 1 2 3 3 3 3 3
The normalized CDF ensures that the intensity values are distributed evenly across the range [0, 7].
Step 3: Apply the Transformation Function:
Use the normalized cumulative histogram as a transformation function to map the original pixel
intensities to new values.
mathematica
Original Image:
0 1 2 3 4 5 6 7
0 0 0 0 0 0 0 0
Equalized Image:
0 1 2 3 4 5 6 7
100 90 50 20 0 0 0 0
Each pixel value in the original image is replaced with its corresponding value from the equalized
histogram.
This equalized image has improved contrast compared to the original image, as the pixel intensities are
redistributed to span the entire intensity range more evenly.
This step-by-step process demonstrates how histogram equalization redistributes pixel intensities to
enhance the contrast of an image. Let me know if you need further clarification on any part!
Define image enhancement and discuss the point operations in image enhancement?
Explain about Homomorphic filtering with necessary equations
Definition of Image Enhancement:

Image enhancement refers to the process of improving the visual appearance or quality of an image to
make it more suitable for a specific application or to better meet human perception. It involves
manipulating the pixel values of an image to emphasize certain features, enhance details, reduce noise,
or adjust overall brightness and contrast. The goal of image enhancement is to produce a visually
pleasing or more informative image for visualization, analysis, or interpretation purposes.
Point Operations in Image Enhancement:

Point operations, also known as pixel-level operations, are simple mathematical transformations applied
independently to each pixel in an image. These operations modify the pixel values based solely on their
current values, without considering neighboring pixels. Point operations are fundamental techniques in
image enhancement and can be broadly categorized into the following types:
1. Brightness and Contrast Adjustment:
Brightness Adjustment: Involves uniformly increasing or decreasing the intensity values of

all pixels in an image to make it brighter or darker, respectively.
Contrast Adjustment: Involves redistributing the intensity values of pixels to increase or
decrease the difference between the lightest and darkest areas of an image, thereby
enhancing the image's contrast.
2. Histogram Stretching:
Histogram stretching, also known as contrast stretching, aims to expand the dynamic range of
pixel intensities in an image to span the entire available intensity range. This is typically achieved
by linearly scaling the pixel values to stretch or compress the histogram along the intensity axis.
3. Histogram Equalization:
Histogram equalization is a technique used to enhance the contrast of an image by redistributing
pixel intensities to achieve a more uniform histogram. It involves computing the cumulative
distribution function (CDF) of pixel intensities and then mapping the original intensity values to
new values based on the CDF.
4. Gamma Correction:
Gamma correction is a nonlinear operation used to adjust the overall brightness and contrast of an
image by applying a power-law transformation to the pixel values. It is commonly used to correct
for non-linearities in display devices or to match the perceived brightness of an image to human
vision.
5. Thresholding:
Thresholding is a technique used to segment an image into regions based on pixel intensity values.
It involves setting pixels with intensity values above or below a specified threshold to a predefined
value, effectively separating foreground and background regions in the image.
Point operations are efficient and computationally inexpensive, making them widely used in various
image processing applications for basic image enhancement tasks.
Homomorphic Filtering:
Homomorphic filtering is a nonlinear image processing technique used to enhance the contrast and
brightness of images corrupted by non-uniform illumination or multiplicative noise. It is particularly
useful for images captured under varying lighting conditions or affected by phenomena such as haze or
glare. Homomorphic filtering works by decomposing an image into its illumination and reflectance
components and then applying filtering operations to modify these components separately before
recombining them to obtain the enhanced image.
The basic equation for homomorphic filtering is given as:
g(x, y) = exp (log(I(x, y)) ∗ H(u, v))
Where:
g(x, y) is the filtered image.

I(x, y) is the input image.
H(u, v) is the homomorphic filter in the frequency domain.
∗ represents the convolution operation.
The homomorphic filter H(u, v) is designed to attenuate low-frequency components (corresponding to

illumination variations) while preserving or enhancing high-frequency components (corresponding to
image details). Common choices for the homomorphic filter include the Butterworth or Gaussian filter in
the frequency domain.
By applying homomorphic filtering, images can be enhanced to improve visibility and reveal hidden
details, making it a valuable tool in various applications such as aerial and satellite imagery analysis,
medical imaging, and surveillance.
Outline the importance of Geometric mean filter.
Explain thinning and thickening operators.
Importance of Geometric Mean Filter:
The geometric mean filter is a valuable tool in image processing for noise reduction, particularly for
images corrupted by multiplicative noise such as speckle noise or grain noise. Here are some reasons
highlighting its importance:
1. Effectiveness Against Multiplicative Noise:

Multiplicative noise tends to corrupt images by modulating pixel values, resulting in grainy or
speckled artifacts. The geometric mean filter is specifically designed to address such noise by
operating in a multiplicative scale, making it more effective in reducing noise while preserving
image details.
2. Preservation of Image Details:

Unlike some other smoothing filters, such as the arithmetic mean filter, which may blur image
details, the geometric mean filter tends to preserve image details while reducing noise. This is
because it computes the mean in a multiplicative scale, giving less weight to extreme values and
resulting in smoother transitions between neighboring pixels.
3. Applications in Medical Imaging and Remote Sensing:

In fields such as medical imaging and remote sensing, where images are often affected by
multiplicative noise sources such as speckle noise in ultrasound images or grain noise in satellite
imagery, the geometric mean filter is indispensable for enhancing image quality and facilitating
accurate analysis and interpretation.
4. Robustness to Outliers:
The geometric mean filter is less sensitive to outliers or extreme pixel values compared to the
arithmetic mean filter. This robustness makes it suitable for processing images with varying noise
levels or with a mixture of noise types.
5. Adaptability to Different Noise Distributions:

The geometric mean filter can adapt to different noise distributions and intensity ranges, making it
versatile for processing images acquired under diverse imaging conditions or with different sensor
characteristics.
Overall, the geometric mean filter is an important tool in the image processing toolbox for effectively
reducing multiplicative noise while preserving image details, making it invaluable for various
applications where noise reduction is critical for accurate analysis and interpretation.
Explanation of Thinning and Thickening Operators:
Thinning:
Thinning is a morphological operation used to reduce the thickness of objects in a binary image to their
skeleton or centerline while preserving their connectivity and topological structure. It works by
iteratively removing pixels from the boundaries of objects until they become as thin as possible while
maintaining the overall shape and connectivity.
Thinning is commonly used in applications such as character recognition, fingerprint analysis, and
medical imaging, where extracting the skeleton or central features of objects is important for
subsequent analysis or comparison.
Thickening:
Thickening is the opposite of thinning and is used to increase the thickness of objects in a binary image
while preserving their shape and connectivity. It works by iteratively adding pixels to the boundaries of
objects until they reach a desired thickness or until certain conditions are met.
Thickening is useful in applications such as image restoration, image segmentation, and morphological
operations, where enhancing or emphasizing the boundaries of objects can improve their visibility or aid
in subsequent processing steps.
These morphological operations, thinning and thickening, play crucial roles in various image processing
tasks and are widely used in conjunction with other techniques to manipulate and analyze binary
images for a wide range of applications.
Explain stereo imaging.
Write a short note on morphological algorithms
Stereo Imaging:
Stereo imaging, also known as stereoscopy or stereo vision, is a technique used to create the perception
of depth in images or videos by presenting slightly different images to each eye. The human visual
system combines these two slightly different views to perceive depth and three-dimensional (3D)
structure in the scene. Stereo imaging simulates the binocular vision of humans, where each eye
captures a slightly different perspective of the same scene.
How Stereo Imaging Works:
1. Capture of Stereo Pair:

Stereo imaging requires the capture of a stereo pair, which consists of two images of the same
scene taken from slightly different viewpoints. These viewpoints mimic the separation between
human eyes.
2. Correspondence Matching:
The next step involves finding corresponding points or features between the two images.
Correspondence matching algorithms are used to identify matching points in the left and right
images based on similarities in intensity, texture, or other features.
3. Depth Estimation:
Once corresponding points are identified, the depth or disparity between the points in the two
images is computed. This depth information is used to estimate the distance of objects in the
scene from the camera.
4. Depth Map Generation:

The depth or disparity information obtained from correspondence matching is often represented
as a depth map, where each pixel value indicates the distance of the corresponding point in the
scene from the camera. Depth maps provide a visual representation of the 3D structure of the
scene.
5. Rendering:
Finally, the depth map is used to render a stereo pair or a 3D view of the scene, where objects
appear to have depth and spatial relationships are accurately portrayed.
Applications of Stereo Imaging:
3D Movies and Entertainment: Stereo imaging is widely used in the entertainment industry to
create 3D movies, games, and virtual reality (VR) experiences, providing viewers with a more
immersive and realistic visual experience.
Depth Sensing and Reconstruction: Stereo imaging is used in robotics, autonomous vehicles, and
depth sensing cameras to accurately perceive the environment and navigate obstacles.
Medical Imaging: In medical imaging, stereo imaging techniques are used for 3D reconstruction
of anatomical structures from medical scans, such as computed tomography (CT) or magnetic
resonance imaging (MRI) images, aiding in diagnosis and treatment planning.
Augmented Reality (AR): Stereo imaging is integral to AR applications, where virtual objects are
superimposed onto the real world, creating the illusion of depth and spatial interaction.
Morphological Algorithms:
Morphological algorithms are a set of image processing techniques based on mathematical

morphology, which deals with the analysis and processing of geometrical structures in images.
Morphological operations manipulate the shape and structure of objects in binary or grayscale images
using set-theoretic operations.
Some common morphological operations include:
Erosion: Shrinks the shapes of objects by removing pixels from their boundaries.
Dilation: Expands the shapes of objects by adding pixels to their boundaries.
Opening: Erosion followed by dilation, useful for removing small objects and smoothing object
boundaries.
Closing: Dilation followed by erosion, useful for filling small gaps and closing holes in objects.
Morphological algorithms are widely used in image segmentation, feature extraction, noise reduction,
and shape analysis tasks. They are particularly useful for processing binary images and are robust to
noise and geometric distortions.
Explain multi-level thresholding.
Write a short not on tree-search.
Multi-level Thresholding:
Multi-level thresholding is an image segmentation technique used to partition an image into multiple
regions or classes based on pixel intensity values. Unlike traditional thresholding, which divides the
image into foreground and background regions using a single threshold value, multi-level thresholding
divides the image into multiple intensity-based classes, each representing a distinct region or object in
the scene.
Process of Multi-level Thresholding:
1. Threshold Selection:
The first step in multi-level thresholding involves selecting appropriate threshold values to divide
the intensity range of the image into multiple intervals or classes. These threshold values can be
determined manually based on prior knowledge of the image content or automatically using
statistical methods or optimization algorithms.
2. Image Segmentation:
Once the threshold values are selected, the image is segmented into multiple regions or classes
based on these thresholds. Each pixel in the image is assigned to one of the classes based on its
intensity value relative to the selected thresholds.
3. Post-processing (Optional):
After segmentation, post-processing techniques may be applied to refine the segmentation results
and improve the accuracy of the segmentation. This may include noise reduction, morphological
operations, or region merging techniques to smooth boundaries and eliminate artifacts.
Applications of Multi-level Thresholding:
Medical Imaging: Multi-level thresholding is used for segmenting medical images such as MRI or
CT scans to identify different anatomical structures or pathologies.
Remote Sensing: In satellite or aerial imagery analysis, multi-level thresholding is used to segment
land cover types, vegetation, water bodies, and urban areas.
Document Analysis: Multi-level thresholding is applied in document image analysis for
segmenting text, graphics, and background regions in scanned documents or handwritten
documents.
Industrial Inspection: In quality control and defect detection applications, multi-level thresholding
is used to segment defects, anomalies, or features of interest in industrial inspection images.
Short Note on Tree-Search:
Tree-search is a fundamental technique used in various fields such as artificial intelligence, operations
research, and computer science for solving problems involving decision-making or optimization. In tree-
search algorithms, a search tree is constructed to represent the possible sequences of decisions or
actions that can be taken to reach a solution or achieve an objective.
Key Concepts of Tree-Search:
State Space Representation: The search space or state space is represented as a tree structure,
where each node represents a state or configuration of the problem, and edges represent
transitions or actions that lead from one state to another.
Search Strategies: Tree-search algorithms employ various search strategies to explore the search
space efficiently and find the optimal solution. Common search strategies include depth-first
search, breadth-first search, heuristic search, and informed search algorithms such as A*.
Node Evaluation: In tree-search algorithms, nodes in the search tree are evaluated based on
certain criteria, such as a cost function, heuristic function, or objective function, to determine their
potential for leading to a solution.
Optimization and Decision Making: Tree-search algorithms are used for optimization problems,
decision-making problems, path planning, game playing, and other tasks where finding the best
sequence of actions or decisions is crucial.
Examples of tree-search algorithms include:
Depth-first search (DFS)

Breadth-first search (BFS)
A* search
Minimax algorithm (used in game playing)
Alpha-beta pruning (used in game playing)
Tree-search algorithms are widely used in various domains, including artificial intelligence, robotics,
operations research, and computer science, to solve complex problems efficiently and systematically.
Write short note on region-based segmentation method.
Region-Based Segmentation Method:
Region-based segmentation is a technique used to partition an image into regions or homogeneous

areas based on certain criteria such as intensity, color, texture, or motion. Unlike edge-based
segmentation, which relies on detecting abrupt changes in pixel values, region-based segmentation
focuses on grouping pixels with similar properties into coherent regions.
Key Concepts of Region-Based Segmentation:
1. Region Homogeneity: In region-based segmentation, the primary criterion for grouping pixels
into regions is homogeneity. A region is considered homogeneous if the pixels within it have
similar characteristics or properties, such as intensity, color, or texture.
2. Region Growing: One of the common approaches to region-based segmentation is region

growing, where the segmentation process starts with seed points or small regions and iteratively
grows these regions by adding neighboring pixels that satisfy certain homogeneity criteria. The
process continues until no more pixels can be added to the region, resulting in segmented regions
that are homogeneous with respect to the specified criteria.
3. Region Merging: Another approach to region-based segmentation is region merging, where the
image is initially divided into small, possibly overlapping regions, and adjacent regions that satisfy
certain homogeneity criteria are merged together to form larger regions. This process continues
until no more merging can be performed, resulting in a set of segmented regions.
4. Homogeneity Criteria: The choice of homogeneity criteria depends on the characteristics of the
image and the specific segmentation task. Common criteria include similarity of intensity values,
color coherence, texture similarity, and motion consistency.
5. Boundary Refinement: After segmentation, post-processing techniques may be applied to refine

the boundaries of segmented regions and improve the segmentation accuracy. This may include
edge detection, morphological operations, or boundary smoothing techniques.
Applications of Region-Based Segmentation:
Medical Imaging: Region-based segmentation is used in medical image analysis for segmenting
anatomical structures, tumors, lesions, and organs from medical images such as MRI, CT, and
ultrasound.
Remote Sensing: In satellite and aerial imagery analysis, region-based segmentation is used for
land cover classification, vegetation mapping, urban area delineation, and object detection.
Object Detection and Tracking: Region-based segmentation is employed in computer vision

applications for object detection, tracking, and recognition in videos and surveillance footage.
Document Analysis: Region-based segmentation is used in document image analysis for

segmenting text, graphics, and background regions in scanned documents or handwritten
documents.
Advantages of Region-Based Segmentation:
Robustness to noise and variations in illumination.

Ability to handle complex scenes with overlapping objects and occlusions.
Facilitation of higher-level analysis tasks such as object recognition and classification.
In summary, region-based segmentation is a powerful technique for partitioning images into coherent
regions based on homogeneity criteria, enabling various image analysis tasks in diverse application
domains.
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Here's a simplified explanation of how it works:

What is Noise? Describe any two noise models in detail.

What is geometric transformation? Also discuss Euclidean transforination.

Salt-and-pepper noise is characterized by sporadic occurrence of very bright or very dark

Now, regarding geometric transformations:

where θ is the angle of rotation.

What are the different stages of DIP? Explain in detail.

3. Image Segmentation: Image segmentation involves partitioning an image into meaningful

Causes of Image Degradation:

Explain the Laplacian image filtering algorithm

Explain histogram equalization. Equalize the given histogram:

No. of Pixels- 790 1023 850 656 329 245 122 81

Here's how the Laplacian filtering algorithm works:

Now, for histogram equalization:

Here's how histogram equalization works:

3. Histogram Equalization Transformation: The histogram equalization transformation function is

To equalize the given histogram:

1. Compute the cumulative distribution function (CDF) from the histogram.

1. Compute the Cumulative Distribution Function (CDF):

Let's compute it:

CDF(5) = 3648 + 245 = 3893

Now, normalize each of these values:

CDFnorm (4) = ≈ 0.891

2. Compute New Intensity Values:

New Intensity(i) = CDFnorm (i) × Max Intensity

New Intensity(5) = 0.951 × 7 ≈ 6.657

3. Apply Transformation to Image:

Compare and constrast smoothing and sharpening filters.

Explain the process of filtering in spatial domain.

Now, regarding the process of filtering in the spatial domain:

Explain thinning and thickening operations with suitable example.

Explain the procedure of region filling with an example.

Now, let's explain the procedure of region filling with an example:

Explain stereo imaging and multi-level thresholding.

Here's how stereo imaging works:

Here's how multi-level thresholding works:

Common edge linking techniques include:

Write a short note on deterministic methods of object recognition.

Basic Block Diagram of Image Restoration Process:

Areas of Application for Image Restoration:

Note on Deterministic Methods of Object Recognition:

1. Feature-Based Matching: Detecting and matching key features or descriptors in images to

draw block daigram using ascii art

+---------------------+ +----------------------+ +---------------------+

Describe the geometric mean filter.

Define the following terms: N4 (p),N8 (p) & Nd (p)

What do mean by color model?

Write the equation for Gaussian low pass filter.

e) What is convex hull?

Define image segmentation.

Explain the process of filtering in spatial domain.

Explain Dilation and erosion

For a 3 bit 4*4 size image perform

(ii)Thresholding with T-4

(iii) Bit plane slicing

Equalize the below histogram

Explain about Homomorphic filtering with necessary equations

Outline the importance of Geometric mean filter.