JP2011147076A - Image processing apparatus, image capturing apparatus and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a means for more promptly making an accurate focusing determination. <P>SOLUTION: An image processing apparatus includes an image acquisition part for acquiring a color captured image; a first feature amount acquisition part, an area setting part, a second feature amount acquisition part, and an edge amount detecting part. The first feature amount acquisition part uses a plurality of color information items of the captured image to determine a first feature amount at each position of the captured image, respectively. The area setting part uses first feature amount distribution information indicating the distribution of the first feature amounts in the capture image to determine a detection area to detect an edge amount in the captured image. The second feature amount acquisition part uses a plurality of color information items of the captured image to determine a second feature amount, different from the first feature amount, at each position of the captured image, respectively. The edge amount detecting part uses second feature amount distribution information indicating the distribution of the second feature amounts in the captured image to detect an edge amount at each position in the detection area. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to an image processing device, an imaging device, and a program.

  2. Description of the Related Art Conventionally, a technique for performing in-focus evaluation of an image acquired by an imaging device using a subject contrast value obtained from an image brightness value or the like is known.

  As an example, in Patent Document 1, in order to suppress the influence of a false edge generated around a high-luminance point light source, a saturation region in an image is extracted using a luminance value, and a contrast value is obtained in a region other than the saturation region. A technique for performing in-focus determination is disclosed. In the example of Patent Document 1, the classification of the shooting scene is executed from the luminance value of the entire image, and the focus determination method is changed depending on the mode (night view mode, normal mode, etc.) according to the classification result.

Japanese Patent Laid-Open No. 2003-262833

  By the way, in the above-described conventional technology, there is room for improvement in that it is difficult to quickly perform highly accurate focus determination because the calculation load of the entire focus determination becomes very large.

  Therefore, an object of the present invention is to provide means for more quickly executing a focus determination with high accuracy.

  An image processing apparatus according to one aspect includes an image acquisition unit that acquires a color captured image, a first feature amount acquisition unit, a region setting unit, a second feature amount acquisition unit, and an edge amount detection unit. The first feature amount acquisition unit obtains a first feature amount at each position of the captured image using a plurality of pieces of color information of the captured image. The region setting unit sets a detection region for detecting an edge amount in the captured image, using the first feature amount distribution information indicating the distribution of the first feature amount in the captured image. The second feature quantity acquisition unit obtains a second feature quantity different from the first feature quantity at each position of the photographed image using a plurality of pieces of color information of the photographed image. The edge amount detection unit detects the edge amount at each position in the detection region using the second feature amount distribution information indicating the distribution of the second feature amount in the captured image.

  In the above aspect, the second feature amount acquisition unit may obtain the second feature amount from the minimum pixel value among a plurality of pixel values corresponding to different color components at each position of the captured image. .

  In the one aspect described above, the first feature quantity acquisition unit may obtain the first feature quantity from the maximum pixel value among a plurality of pixel values corresponding to different color components at each position of the captured image. .

  In the one aspect, the image acquisition unit performs pixel addition processing on an image before color interpolation in which each pixel has single color information, and acquires the captured image in which each pixel has information on multiple colors. Also good.

  The image processing apparatus according to the one aspect may further include a focus calculation unit that obtains a focus position of the captured image based on the edge amount detected by the edge amount detection unit.

  In addition, an imaging apparatus including the above-described image processing apparatus, a program that causes a computer to operate as the image processing apparatus according to the one aspect, a storage medium that stores the program, and an image processing apparatus according to the one aspect An aspect in which the operation is expressed by a method category is also effective as a specific aspect of the present invention.

  In one aspect of the present invention, a detection region of a captured image is set using a first feature amount based on a plurality of color information, and an edge amount is detected from the detection region using a second feature amount different from the first feature amount. By doing so, a highly accurate focus determination can be performed more quickly.

1 is a block diagram illustrating a configuration example of an image processing apparatus according to an embodiment. The flowchart which shows the operation example of the focus evaluation process in one Embodiment The figure which shows an example of the photographed image The figure which shows the example of the effective area map corresponding to the picked-up image of FIG. The block diagram which shows the structural example of the electronic camera in other embodiment. The flowchart which shows the operation example in imaging | photography mode in the electronic camera of other embodiment

<Description of one embodiment>
FIG. 1 is a block diagram illustrating a configuration example of an image processing apparatus according to an embodiment. The image processing apparatus according to one embodiment is a personal computer in which an image processing program is installed. A computer constituting the image processing apparatus reads an image file generated by an imaging apparatus such as an electronic camera by executing an image processing program, and performs a focus evaluation process on a captured image of the image file.

  Here, the image file to be processed in one embodiment is generated in a file format conforming to, for example, the Exif (Exchangeable image file format for digital still cameras) standard. In the image file to be processed in one embodiment, a color photographed image photographed by the imaging device, photographing information related to the photographed image (position information of a focus detection area by autofocus (AF), and the like) Are stored in association with each other.

  A computer 11 illustrated in FIG. 1 includes a data reading unit 12, a storage device 13, a CPU 14, a memory 15, an input / output I / F 16, and a bus 17. The data reading unit 12, the storage device 13, the CPU 14, the memory 15, and the input / output I / F 16 are connected to each other via a bus 17. Further, an input device 18 (keyboard, pointing device, etc.) and a monitor 19 are connected to the computer 11 via an input / output I / F 16. The input / output I / F 16 receives various inputs from the input device 18 and outputs display data to the monitor 19.

  The data reading unit 12 is used when reading the image file or the image processing program from the outside. For example, the data reading unit 12 communicates with a reading device (such as an optical disk, a magnetic disk, or a magneto-optical disk reading device) that acquires data from a removable storage medium, or an external device in accordance with a known communication standard. It consists of communication devices (USB interface, LAN module, wireless LAN module, etc.) to be performed.

  The storage device 13 is configured by a storage medium such as a hard disk or a nonvolatile semiconductor memory. The storage device 13 stores the image processing program and various data necessary for executing the program. The storage device 13 can also store the image file read from the data reading unit 12.

  The CPU 14 is a processor that comprehensively controls each unit of the computer 11. The CPU 14 functions as a first feature amount acquisition unit 21, a region setting unit 22, a second feature amount acquisition unit 23, an edge amount detection unit 24, and a focus calculation unit 25 by executing the image processing program (note that the CPU 14). (The operation of each part included in will be described later).

  The memory 15 temporarily stores the calculation result of the image processing program. The memory 15 is composed of, for example, a volatile SDRAM.

  Next, an operation example of the focus evaluation process in one embodiment will be described with reference to the flowchart of FIG. 2 is started when the CPU 14 executes the image processing program in accordance with a program execution instruction from the user.

  Step S101: The CPU 14 acquires an image file to be processed from the outside via the data reading unit 12. The image file acquired in S101 is recorded in the storage device 13 or the memory 15 under the control of the CPU 14. Note that when the image file to be processed is stored in the storage device 13 in advance, the CPU 14 may omit the process of S101.

  Step S102: The CPU 14 converts the captured image included in the image file of S101 into an image having color information of R (red), G (green), and B (blue) at each pixel, as necessary. In the following processing, the image after the conversion in S102 is a processing target. If the captured image has RGB color information for each pixel in advance, the CPU 14 may omit the process of S102.

  In S102, when the captured image data is in the YCbCr format, the CPU 14 generates the captured image data in the RGB format by converting the color space. In addition, when an image having a Bayer array structure before color interpolation, in which each pixel has only any single color information of RGB, is input, the CPU 14 executes color interpolation processing. Thereby, the picked-up image which has the information of each RGB color in each pixel can be acquired.

  In addition, when an image having the above-described Bayer array structure is input in S102, the CPU 14 may execute a pixel addition process that bundles a plurality of adjacent pixels into one pixel. Thereby, it is possible to easily obtain a captured image having a resolution lower than that of the original image and having RGB pixel values in each pixel.

  As an example, the CPU 14 generates RGB pixel values for one pixel of the converted captured image from a 2 × 2 pixel range of the original image having a Bayer array structure. At this time, the pixel values of the R pixel and the B pixel of the original image become the R and B pixel values as they are in the converted captured image. Further, the G pixel value in the converted captured image may be obtained by averaging the pixel values of the Gr pixel and Gb pixel of the original image.

  FIG. 3 shows an example of a captured image to be processed. The image in FIG. 3 is a night view taken with a streetlight on the right edge of the screen and a bridge illuminated with red and blue illuminations on the top of the screen. Also, a color-coded signboard is shown in the center of the image in FIG. 3, and a subject (tree) that is out of focus is shown on the left side of the image in FIG. Note that the image in FIG. 3 is most focused on the color-coded signboard.

  Step S103: The first feature quantity acquisition unit 21 obtains a first feature quantity for each pixel of the captured image using the RGB color information of each pixel. Specifically, the first feature value acquisition unit 21 sets the maximum value among the RGB pixel values as the first feature value in each pixel of the captured image. For example, when the R pixel value is the highest at the target pixel of the captured image, the first feature amount at the target pixel is the R pixel value. Further, when the entire captured image is viewed, the first feature amount can be extracted from a different color for each pixel.

  Then, the first feature quantity acquisition unit 21 generates a first feature quantity distribution map indicating the correspondence between the pixel position on the captured image and the first feature quantity. Since the first feature quantity distribution map indicates the first feature quantity of each pixel of the photographed image, the size of the first feature quantity distribution map is the same as the image size of the photographed image to be processed.

  Step S104: The region setting unit 22 sets a detection region for detecting an edge amount on the captured image using the first feature amount distribution map (S103). As an example, the area setting unit 22 in S104 executes the following processes (a) to (c).

  (A) The region setting unit 22 extracts saturated pixels in which the value of the first feature value is equal to or greater than a threshold value in the first feature value distribution map. As an example, when the gradation of the pixel value in the photographed image is 8 bits (0-255), the threshold value is set to a value of about 250.

  (B) The region setting unit 22 sets a pixel within a predetermined distance from the saturated pixel on the captured image as a pseudo saturated pixel. As an example, the region setting unit 22 sets pixels within a radius of 5 pixels from saturated pixels as pseudo-saturated pixels.

  (C) The region setting unit 22 sets the remaining pixel region on the captured image excluding the saturated pixel and the pseudo-saturated pixel as the detection region. Then, the area setting unit 22 generates binary image information (effective area map) indicating a detection area on the captured image and other non-detection areas (saturated pixels and pseudo-saturated pixels).

  In the above effective area map, the pseudo saturated pixels set around the saturated pixels are excluded from the detection area, so that it is possible to suppress the influence of the pseudo contour generated around the bright spot during the focus evaluation.

  FIG. 4 shows an example of an effective area map corresponding to the captured image of FIG. In the captured image of FIG. 3, the pixel value of any one of the color components of the illumination point light source and the streetlight is high, and even if the colors are different from each other, the pixel becomes a saturated pixel by the determination with the first feature amount. Therefore, in the effective area map of FIG. 4, an area excluding the area around the illumination point light source and the streetlight is set as the detection area.

  Step S105: The second feature quantity acquisition unit 23 obtains a second feature quantity for each pixel of the captured image using the RGB color information of each pixel. Specifically, the second feature value acquisition unit 23 sets the minimum value of the RGB pixel values as the second feature value in each pixel of the captured image. For example, when the pixel value of B is the smallest among the target pixels of the captured image, the second feature amount at the target pixel is the B pixel value. Further, when the entire captured image is viewed, the second feature amount can be extracted from a different color for each pixel.

  Then, the second feature quantity acquisition unit 23 generates a second feature quantity distribution map indicating the correspondence between the pixel position on the captured image and the second feature quantity. Since the second feature quantity distribution map indicates the second feature quantity of each pixel of the photographed image, the size of the second feature quantity distribution map is the same as the image size of the photographed image to be processed.

  Step S106: The edge amount detection unit 24 detects the edge amount (the amount indicating the edge component of the captured image) at each pixel position in the captured image using the second feature amount distribution map (S105). Then, the edge amount detection unit 24 generates an edge amount map indicating the edge amount at each pixel of the captured image.

  As an example of the processing in S106, an example in which an edge amount is detected using an image structure vector will be described. First, the edge amount detection unit 24 sets a predetermined local region in the captured image around the target pixel for which the edge amount is obtained. Next, the edge amount detection unit 24 obtains an image gradient vector of the target pixel from the gradient of the second feature amount of the pixels included in the local region using a principal component analysis technique.

Here, for each pixel x _i in the local area of the captured image, the gradient of the pixel value is set.

Can be obtained by solving the eigenvalue problem. However, “C” in the above equation (2) indicates a matrix defined by the following equation (3) using the pixel value f (x _i ) at each pixel x _i in the local region.

Note that “Σ” in Equation (3) above represents the sum of all the pixels included in the local region.

  Further, since the matrix C is a 2 × 2 matrix, the above equation (2) has two eigenvalues and two different unit eigenvectors.

  The edge amount detection unit 24 performs the above operation on each pixel, and acquires image structure vector information on each pixel. Then, the edge amount detection unit 24 obtains the edge amount of each pixel from the above image structure vector information. For example, the edge amount detection unit 24 may obtain the edge amount of each pixel using the intensity of the image structure vector. Note that the image structure vector is obtained from the gradient of the pixel values in the local region using a principal component analysis technique, so that the influence of image noise can be extremely reduced during focus evaluation.

  Alternatively, the edge amount detection unit 24 may obtain the edge amount for each pixel by using the inner product of the image structure vectors. For an object in focus in the image, the correlation of the image structure is high, and the image structure vectors of the pixel of interest and the surrounding pixels are easily oriented in the same direction. On the other hand, for an out-of-focus subject, the image is blurred and the directionality of the structure is lost, and the orientation of the image structure vector is non-uniform compared to the focused state. Therefore, it is understood that the focus evaluation of the captured image can be performed by looking at the correlation of the direction of the image structure vector between adjacent target pixels (the value of the inner product of the image structure vectors).

  As another example of the processing in S106, the edge amount detection unit 24 calculates the absolute value of the difference value of the second feature amount between the target pixel and the surrounding pixels, and calculates the sum of the absolute values as the target pixel. It is good also as edge amount in. Alternatively, as another example of the processing in S106, the edge amount detection unit 24 obtains the absolute value of the difference value of the second feature amount between the target pixel and the surrounding pixels, and the maximum value among the absolute values. May be extracted to determine the edge amount of the pixel of interest.

  In the flowchart of FIG. 2, the processing from S <b> 103 to S <b> 106 is described as being performed in series, but the image processing apparatus may perform the processing from S <b> 103 to S <b> 104 and the processing from S <b> 105 to S <b> 106 in parallel. .

  Here, when attention is paid to the minimum value of the RGB pixel value (second feature amount) at a location where there is a luminance difference in the detection area, even in the same color component, the minimum value of the pixel value of the adjacent pixel changes due to the luminance difference. Therefore, it can be seen that by using the second feature amount, an edge due to a luminance difference between adjacent pixels can be extracted.

  Similarly, when attention is paid to the second feature amount in a portion having a color difference in the detection region, since the color of each pixel is different, the value of the second feature amount is completely different between adjacent pixels. Therefore, it can be seen that by using the second feature amount, the edge can be extracted also from a subject (for example, the color boundary of the signboard of FIG. 3) whose edge can be detected by looking at the color difference although the luminance difference is small.

  Step S107: The CPU 14 uses the effective area map (S104) and the edge amount map (S106) to extract the edge amount of each pixel in the detection area of the captured image. Specifically, the CPU 14 masks the edge amount map with the effective area map, thereby extracting pixel data of the detection region from the edge amount map.

  Step S108: The focus calculation unit 25 uses the edge amount (S107) of each pixel in the detection region to extract a pixel position having the largest edge amount (that is, the highest focus degree) in the detection region. To do.

  Step S109: The focusing calculation unit 25 performs focus evaluation of the captured image. As an example, the focus calculation unit 25 acquires the position information of the focus detection area from the image file. Then, the focus calculation unit 25 obtains a linear distance between the position of the focus detection area of the photographed image and the focus position in the detection area obtained in S108. When the straight line distance can be regarded as almost zero, it can be determined that the captured image to be processed is in focus on the main subject. On the other hand, if the linear distance is greater than the allowable range, it can be determined that the captured image to be processed is not in focus on the main subject.

  Thereby, the focus calculation unit 25 can evaluate the in-focus state of the photographed image using the magnitude of the linear distance obtained in S109 as an index, and can automatically select an image in good in-focus state. Note that the focus calculation unit 25 may only present information on the magnitude of the linear distance obtained in S109 to the user. Above, description of the flowchart of FIG. 2 is complete | finished.

  Hereinafter, the effect of the focus evaluation processing in one embodiment will be described.

  The image processing apparatus according to one embodiment sets the maximum value of the RGB pixel value for each pixel of the captured image as the first feature amount (S103). Based on the first feature amount, the image processing apparatus detects a region in which the pixel value is saturated with any color component in the captured image (S104). Then, the image processing apparatus performs focus evaluation of the captured image with respect to the detection area excluding the saturated pixel and the periphery thereof (S107 to S109).

  In other words, the image processing apparatus according to the embodiment does not saturate with the luminance value, but uses a red or blue point light source (for example, the illumination in FIG. 3) in which the pixel value is saturated when the color information is viewed. In addition, it can be excluded from the focus determination target together with the high-luminance subject. Thereby, in one embodiment, compared with the case where focus determination is performed by focusing only on the luminance value of the captured image, the possibility of falsely determining a false edge due to a blurred point light source as the in-focus is reduced and more accurate. It is possible to perform a high focus determination.

  Further, the image processing apparatus according to the embodiment sets the minimum value of the RGB pixel value as the second feature amount in each pixel of the captured image (S105). Then, the image processing apparatus performs focus evaluation of the captured image based on the edge amount obtained from the second feature amount with respect to the detection region (S106 to S109). Thereby, in one embodiment, the edge by the brightness | luminance difference of an adjacent pixel and the edge by the color difference of an adjacent pixel can each be extracted with a 2nd feature-value.

Further, the image processing apparatus according to the embodiment applies two types of parameters (first feature amount and second feature amount) set using a plurality of pieces of color information, and performs detection region setting processing. In-focus determination processing using the edge amount is performed. Thereby, the image processing apparatus in one embodiment
For example, it is possible to accurately evaluate an image suitable for focus determination processing on the luminance plane and an image suitable for focus determination processing on the color difference plane by the same processing.

  Further, as in the prior art, when performing the focus determination process with one type of parameter, switching between the focus determination process on the luminance plane and the focus determination process on the color difference plane according to the analysis result of the shooting scene In addition, processing such as changing the weighting of the determination result is required. On the other hand, the image processing apparatus of one embodiment can deal with various captured images with the same processing, and therefore can perform highly accurate focus determination quickly.

<Description of other embodiments>
FIG. 5 is a block diagram illustrating a configuration example of an electronic camera according to another embodiment. The electronic camera 31 includes a focusing lens 32, a lens driving unit 33, an image sensor 34, a control unit 35, a ROM 36, a main memory 37, a monitor 38, a recording I / F 39, and a release button 40. is doing. Here, the lens driving unit 33, the image sensor 34, the ROM 36, the main memory 37, the monitor 38, the recording I / F 39, and the release button 40 are respectively connected to the control unit 35.

  The focusing lens 32 is a lens for performing focus adjustment. The lens position of the focusing lens 32 is adjusted by the lens driving unit 33 in the optical axis direction.

  The image sensor 34 captures an image of a subject formed by an imaging optical system including the focusing lens 32 and generates an image signal of a color captured image. The image signal output from the image sensor 34 is input to the control unit 35 via an A / D conversion circuit (not shown).

  Here, in the photographing mode of the electronic camera 31, the image sensor 34 captures a still image for recording (main image) in response to a full press operation of the release button 40. Further, the imaging device 34 in the shooting mode continuously takes images for observation (through images) at predetermined intervals even during standby for shooting. Here, the data of the through image acquired in time series is used for moving image display on the monitor 38 and various arithmetic processes by the control unit 35.

  The control unit 35 is a processor that comprehensively controls the operation of the electronic camera 31. For example, the control unit 35 performs various types of image processing (color interpolation processing, gradation conversion processing, contour enhancement processing, white balance adjustment, color conversion processing, etc.) on the captured image data.

  In addition, the control unit 35 executes the program stored in the ROM 36 to thereby detect the first feature amount acquisition unit 21, the region setting unit 22, the second feature amount acquisition unit 23, and the edge amount detection in the image processing apparatus according to the embodiment. Functions as the unit 24 and the focus calculation unit 25.

  The ROM 36 stores a program executed by the control unit 35. An example of the operation in the shooting mode by this program will be described later. The main memory 37 temporarily stores image data in the pre-process and post-process of image processing by the control unit 35. The monitor 38 displays various images according to instructions from the control unit 35.

  The recording I / F 39 has a connector for connecting a nonvolatile storage medium 39a. The recording I / F 39 executes data writing / reading with respect to the storage medium 39a connected to the connector. The storage medium 39a is composed of a hard disk, a memory card incorporating a semiconductor memory, or the like. In FIG. 5, a memory card is illustrated as an example of the storage medium 39a.

  The release button 40 receives from the user an instruction input for starting an AF operation before photographing by a half-press operation and an instruction input for starting an imaging operation by a full-press operation.

  Next, with reference to the flowchart of FIG. 6, an operation example in the shooting mode in the electronic camera 31 of another embodiment will be described. Note that the processing of the flowchart of FIG. 6 is started in response to a user's shooting mode activation operation under the control of the control unit 35.

  Step S201: The control unit 35 drives the imaging device 34 to start capturing a through image. Thereafter, the through images are sequentially generated at predetermined intervals. The through image data output from the image sensor 34 is input to the control unit 35.

  Further, the control unit 35 in the shooting mode causes the monitor 38 to display a moving image of the display image (view image) generated from the through image. Therefore, the user can perform framing for determining the shooting composition with reference to the view image on the monitor 38.

  Step S202: The control unit 35 determines whether or not a half-press operation of the release button 40 has been accepted. If the above requirement is satisfied (YES side), the process proceeds to S203. On the other hand, when the above requirement is not satisfied (NO side), the control unit 35 waits for a half-press operation of the release button 40.

  Step S203: The control unit 35 performs focusing control using the through image.

  As an example, the control unit 35 acquires a through image at each lens position while moving the focusing lens 32. And the control part 35 performs the process of S101 to S108 in said one embodiment for each through image. In addition, the control unit 35 sets a focus detection area in a shooting screen (shooting range of a through image) based on an algorithm such as closest priority or center priority (or designation by a user's manual). Then, the control unit 35 performs AF of the focusing lens 32 by using the lens position of the through image in which the focus degree of the subject in the focus detection area is the highest.

  Step S204: The control unit 35 determines whether or not a full pressing operation of the release button 40 has been accepted. If the above requirement is satisfied (YES side), the process proceeds to S206. On the other hand, if the above requirement is not satisfied (NO side), the process proceeds to S205.

  Step S205: The control unit 35 determines whether or not the half-press operation of the release button 40 has been released. If the above requirement is satisfied (YES side), the control unit 35 returns to S202 and repeats the above operation. On the other hand, if the above requirement is not satisfied (NO side), the control unit 35 returns to S204 and waits for a full pressing operation of the release button 40.

  Step S206: In response to the release button 40 being fully pressed, the control unit 35 drives the imaging device 34 to execute the imaging process for the main image. The data of the main image is recorded on the storage medium 39a via the recording I / F 39 after a predetermined process is performed by the control unit 35. Above, description of the flowchart of FIG. 6 is complete | finished.

  The electronic camera 31 of the other embodiment executes AF by an algorithm almost the same as that of the first embodiment. Therefore, the electronic camera 31 of the other embodiment can quickly execute the focus determination with high accuracy as in the case of the one embodiment.

<Supplementary items of the embodiment>
(1) In each of the embodiments described above, the functions of the first feature value acquisition unit 21, the region setting unit 22, the second feature value acquisition unit 23, the edge amount detection unit 24, and the focus calculation unit 25 are implemented by software. In the above example, the above-described units may be realized by hardware using an ASIC.

  (2) In each of the above-described embodiments, an example in which the first feature value and the second feature value are obtained using RGB pixel values has been described. For example, a complementary color system (for example, a system using magenta, cyan, yellow) Information may be used.

  (3) In the processing of S104 of one embodiment, an example in which the threshold value for determining a saturated pixel is set to 250 with 8 bits has been described, but the above threshold value may be changed as appropriate. Moreover, in the process of S104 of one embodiment, the range of pseudo-saturated pixels may be changed as appropriate according to the captured image.

  (4) In the processing of S102 of one embodiment, a case has been described in which an image with a Bayer array structure is 2 × 2 pixels and 1 pixel. However, a larger number of pixels (such as 4 × 4 pixels) are combined into 1 pixel. The information may be generated.

  (5) In the one embodiment described above, the image processing apparatus may execute the process of S106 after specifying the detection area in the captured image in advance with the effective area map. In the above-described one embodiment, the image processing apparatus may execute the focus evaluation process for the range of the focus detection area in the captured image.

  (6) In the other embodiments described above, the configuration example of the compact electronic camera including the photographing lens as a component has been described. However, the imaging apparatus of the present invention can of course be applied to a single-lens reflex type electronic camera in which lenses can be exchanged. The imaging device of the present invention can also be applied to a camera module built in an electronic device such as a mobile phone.

  From the above detailed description, features and advantages of the embodiments will become apparent. It is intended that the scope of the claims extend to the features and advantages of the embodiments as described above without departing from the spirit and scope of the right. Further, any person having ordinary knowledge in the technical field should be able to easily come up with any improvements and modifications, and there is no intention to limit the scope of the embodiments having the invention to those described above. It is also possible to use appropriate improvements and equivalents within the scope disclosed in.

DESCRIPTION OF SYMBOLS 11 ... Computer, 12 ... Data reading part, 13 ... Memory | storage device, 14 ... CPU, 15 ... Memory, 16 ... Input-output I / F, 17 ... Bus, 18 ... Input device, 19 ... Monitor, 21 ... 1st feature-value Acquisition unit, 22 ... area setting unit, 23 ... second feature quantity acquisition unit, 24 ... edge amount detection unit, 25 ... focusing calculation unit, 31 ... electronic camera, 32 ... focusing lens, 33 ... lens drive unit, 34 ... Image sensor 35 ... Control unit 36 ... ROM 37 ... Main memory 38 ... Monitor 39 ... Recording I / F 39a ... Storage medium 40 ... Release button

Claims (7)

An image acquisition unit for acquiring a color photographed image;
A first feature amount acquisition unit that obtains a first feature amount at each position of the captured image using a plurality of color information of the captured image;
An area setting unit that sets a detection area for detecting an edge amount in the photographed image using first feature quantity distribution information indicating a distribution of the first feature quantity in the photographed image;
A second feature amount acquisition unit that obtains a second feature amount different from the first feature amount at each position of the captured image using a plurality of pieces of color information of the captured image;
An edge amount detection unit that detects an edge amount at each position in the detection region using second feature amount distribution information indicating the distribution of the second feature amount in the captured image;
An image processing apparatus comprising: The image processing apparatus according to claim 1.
The second feature quantity acquisition unit is an image processing apparatus that obtains the second feature quantity from a minimum pixel value among a plurality of pixel values each corresponding to a different color component at each position of the captured image. The image processing apparatus according to claim 1 or 2,
The first feature quantity acquisition unit is an image processing apparatus that obtains the first feature quantity from a maximum pixel value among a plurality of pixel values corresponding to different color components at each position of the captured image.
Image processing device. The image processing apparatus according to any one of claims 1 to 3,
The image acquisition unit is an image processing apparatus that performs pixel addition processing on an image before color interpolation in which each pixel has single-color information, and acquires the captured image in which each pixel has information on a plurality of colors. The image processing apparatus according to claim 1, wherein:
An image processing apparatus further comprising a focus calculation unit that obtains a focus position of the captured image based on the edge amount detected by the edge amount detection unit. An imaging unit that captures an image of the light flux that has passed through the taking lens;
An image processing apparatus according to claim 5;
An imaging apparatus comprising: a control unit that controls a focused state of the photographing lens based on an output of the image processing apparatus.   A program that causes a computer to operate as the image processing apparatus according to any one of claims 1 to 5.

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