Nothing Special   »   [go: up one dir, main page]

Gantenapalli et al., 2023 - Google Patents

Selective mean filtering for reducing impulse noise in digital color images

Gantenapalli et al., 2023

Document ID
2716787961725847322
Author
Gantenapalli S
Choppala P
Meka J
Publication year
Publication venue
International Journal of Image and Graphics

External Links

Snippet

The interest of this paper is in reduction of impulse noise in digital color images. The two main methods used for noise reduction in images are the mean and median filters. These techniques operate by replacing the test pixel in a chosen window by a new filtered pixel …
Continue reading at www.worldscientific.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20048—Transform domain processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G06T5/001—Image restoration
    • G06T5/002—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20172—Image enhancement details
    • G06T2207/20182—Noise reduction or smoothing in the temporal domain; Spatio-temporal filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G06T5/20—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image by the use of local operators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6201—Matching; Proximity measures
    • G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00496—Recognising patterns in signals and combinations thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations

Similar Documents

Publication Publication Date Title
Gagnon et al. Speckle noise reduction of airborne SAR images with symmetric daubechies wavelets
Zhang et al. Joint image denoising using adaptive principal component analysis and self-similarity
CN114677300B (en) Method and system for deep noise reduction of hyperspectral image based on double-stage learning framework
CN109636766B (en) Edge information enhancement-based polarization difference and light intensity image multi-scale fusion method
Wei Image super‐resolution reconstruction using the high‐order derivative interpolation associated with fractional filter functions
CN112508810A (en) Non-local mean blind image denoising method, system and device
CN113837974B (en) NSST domain power equipment infrared image enhancement method based on improved BEEPS filtering algorithm
Teng et al. Modified pyramid dual tree direction filter‐based image denoising via curvature scale and nonlocal mean multigrade remnant filter
CN105913382B (en) The high-fidelity anisotropic filtering method of threshold value optimizing
Gopinathan et al. Wavelet and FFT Based Image Denoising Using Non-Linear Filters.
CN111242854A (en) Image denoising method
CN112927135A (en) Edge perception-based polarized image interpolation method
Gantenapalli et al. Selective mean filtering for reducing impulse noise in digital color images
Li et al. Multipatch unbiased distance non-local adaptive means with wavelet shrinkage
CN107610056B (en) Mixed weighting wiener filtering image denoising method based on total variation
CN111461999A (en) SAR image speckle suppression method based on super-pixel similarity measurement
Potocnik et al. Image enhancement by using directional wavelet transform
Ye et al. Image enhancement method based on bilinear interpolating and wavelet transform
CN115034978B (en) Hyperspectral image denoising method based on subspace representation and graph embedding constraint
CN113484913B (en) Seismic data denoising method for multi-granularity feature fusion convolutional neural network
CN115689958A (en) Synthetic radar image denoising method based on deep learning
CN115657140A (en) Magnetic anomaly detection method and system based on structured Hankel total variation regularization
Jia et al. Fractional‐integral‐operator‐based improved SVM for filtering salt‐and‐pepper noise
Zhang et al. Multi-noise and multi-channel derived prior information for grayscale image restoration
Wang et al. Shannon‐Cosine Wavelet Precise Integration Method for Locust Slice Image Mixed Denoising