JP7383891B2 - Image processing device, image processing method, and program - Google Patents

Description

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

Conventionally, techniques for correcting facial images have been proposed. For example, Patent Document 1 describes a face image in which a smooth skin condition is reproduced by masking characteristic areas such as the eyes, nose, and mouth and applying smoothing processing to the remaining skin areas to make small rough skin less noticeable. It is described that it generates.

Japanese Patent Application Publication No. 2009-70260

However, with the technology of Patent Document 1, it is difficult to correct only specific areas that are different from the surrounding skin, and when correction processing is applied, there is a foreseeable problem that the difference from the surrounding areas that have not been corrected becomes noticeable. be done.

The present invention has been made in view of these problems, and an object of the present invention is to make the corrected area less noticeable when a subject image is corrected.

In order to solve the above problems, the image processing device of the present invention includes:
an acquisition means for acquiring a subject image;
edge component acquisition means for acquiring edge components in a predetermined frequency band in the image region of the subject image acquired by the acquisition means;
Texture information acquisition means for acquiring the edge component acquired by the edge component acquisition means as texture information;
Detection means for detecting a unique region different from the surrounding area in the subject image;
processing means that performs a correction process to make the specific area in the subject image less noticeable ;
processing means for processing the subject image subjected to the correction processing by the processing means, based on the texture information acquired by the texture information acquisition means;
Equipped with
The processing means includes:
Using mask data of a plurality of sizes imitating the shape surrounding the singular region, predetermined image processing is performed on the image area including the singular region surrounded by the mask data, and the mask data of the plurality of sizes are obtained. reliability information acquisition means for acquiring reliability information for each of the image regions subjected to the predetermined image processing using each of the above;
compositing means for composing the image regions subjected to the predetermined image processing so that the degree of influence of the image region for which reliability information acquired by the reliability information acquisition means is determined to be higher is higher;
It is characterized by having the following.

Further, the image processing device of the present invention includes:
an acquisition means for acquiring a subject image;
detection means for detecting a unique region different from the surrounding area in the subject image acquired by the acquisition means;
processing means for applying predetermined processing to image regions including the singular region surrounded by the mask data using mask data of a plurality of sizes imitating shapes surrounding the singular region detected by the detection means;
Reliability information acquisition means for acquiring reliability information for each of the image regions subjected to the predetermined processing using each of the mask data of the plurality of types of sizes;
Synthesis of image regions subjected to the predetermined processing by the processing means so that the degree of influence of the image region determined to have higher reliability information acquired by the reliability information acquisition means is higher. means and
It is characterized by having the following .

According to the present invention, it is possible to make a corrected part less noticeable when a subject image is corrected.

FIG. 1 is a block diagram showing the functional configuration of an imaging device according to the present embodiment. FIG. 2 is a diagram for explaining an outline of processing in an analysis section and a second image processing section in FIG. 1; FIG. 2 is a diagram showing the flow of processing in an analysis section and a second image processing section in FIG. 1;

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. Note that the present invention is not limited to the illustrated example.

[Configuration of imaging device 1]
FIG. 1 is a block diagram showing the functional configuration of an imaging device 1 according to this embodiment. As shown in FIG. 1, the imaging device 1 includes a control section 101, a storage section 102, a display section 103, a communication section 104, an operation section 105, an imaging section 106, a first image processing section 107, an image memory 108, and an analysis section. 109, a second image processing section 110, a lighting control section 111, a light emitting section 112, and the like. The control section 101 and each section are connected by a bus.

The control unit 101 includes a CPU (Central Processing Unit), a RAM (Random Access Memory), and the like, and executes various programs stored in the storage unit 102 to perform predetermined calculations and control each unit.

The storage unit 102 is composed of a nonvolatile semiconductor memory, a hard disk, or the like. The storage unit 102 stores system programs and various processing programs executed by the control unit 101, data necessary for executing these programs, and the like.

The display unit 103 is configured with an LCD (Liquid Crystal Display) or the like, and displays, for example, a live view image showing the state imaged by the imaging unit 106 according to a display control signal from the control unit 101, or displays the image captured by the imaging unit in accordance with a shooting instruction. The image etc. acquired by 106 are displayed.

The communication unit 104 is an interface for wirelessly or wired data communication with external equipment.

The operation unit 105 includes various function buttons such as a shutter key, and a transparent touch panel stacked on the display unit 103, and receives inputs from the user by pressing each button, touching the touch panel, sliding operations, etc., and performs the operations. The information is output to the control unit 101.

The imaging unit 106 includes an imaging lens 106a, an imaging element such as an image sensor such as a CCD (Charge Coupled Device) or a CMOS (Complementary Metal-Oxide Semiconductor) (not shown), an A/D conversion circuit, etc. The optical image that has passed through 106a is converted into a two-dimensional image signal by an image sensor, and image data (RGB image data) of the subject is obtained.

The first image processing unit 107 performs color process processing including pixel interpolation processing and γ correction processing on the RGB data acquired by the imaging unit 106, and then generates a digital luminance signal Y and color difference signals Cb and Cr. Convert to image data (image data of each component of Y, U, and V).

The image memory 108 is configured by, for example, a flash memory or the like, and is a recording unit that records image data captured by the imaging unit 106 and processed by the first image processing unit 107.

The analysis unit 109 analyzes the image data of the subject (subject image) acquired by the imaging unit 106 and processed by the first image processing unit 107 when the correction mode according to the present invention is set in advance. acquires skin texture information (skin surface information) of the subject and outputs it to the second image processing unit 110. The analysis unit 109 also analyzes image areas around specific areas different from the surrounding skin (for example, areas where foreign objects such as pimples are present) in the image data processed by the first image processing unit 107. As a result, texture protection maps M11 and M12 and shadow protection maps M21 and M22 (see FIG. 2) are generated, which are used when the second image processing unit 110 performs correction processing on the unique region. Here, the texture protection maps M11 and M12 and the shadow protection maps M21 and M22 protect image areas that are not subject to correction among the image areas surrounding the specific area when performing correction processing on the specific area. This is mask data for masking.
The analysis unit 109 functions as a texture information acquisition means, an edge component acquisition means, a detection means, an analysis means, and a correction means.

The second image processing unit 110 generates texture protection maps M11, M12, which are generated by the analysis unit 109 for the unique region of the image data acquired by the imaging unit 106 and processed by the first image processing unit 107, A corrected image is generated by performing correction processing such as smoothing processing using the and shadow protection maps M21 and M22, adding texture information to the processing result, and combining it with the original image data (original image). .
The second image processing section 110 functions as a processing means and a processing means.

The analysis unit 109 and the second image processing unit 110 are assumed to be executed in cooperation with the control unit 101 and the program stored in the storage unit 102, but may also be realized by dedicated hardware. .

The lighting control unit 111 (driver) is connected to each of the LEDs (Light Emitting Diodes) of the light emitting unit 112, and controls the light amount and turning on/off of the light emitting unit 112 according to instructions from the control unit 101.

The light emitting unit 112 is composed of an LED or the like, and irradiates the subject with light.

[Operation of imaging device 1]
Next, the operations of the analysis section 109 and the second image processing section 110 of the imaging device 1 will be explained.
FIG. 2 is a diagram schematically showing processing executed by the analysis unit 109 and the second image processing unit 110. G1 in FIG. 2 is a diagram schematically showing the unique region and the surrounding Y component image (luminance component image) in the original image, using the image data processed by the first image processing unit 107 as the original image. . In G1 of FIG. 2, a diagonal line 51 represents a unique region, a dot 52 represents the texture of the skin, and a shaded area 53 represents a shadow on the skin. This shadow may be caused by unevenness caused by facial organs or wrinkles, for example, or may be caused by hair, eyelashes, eyebrows, or their shadows. In FIG. 2, a texture 52 exists throughout G1. In addition to the unique region 51 (an image of a foreign object such as a pimple), a shadow 53 exists around the unique region.

If a correction process is performed to make the specific area 51 less noticeable in the original image corresponding to this Y component image G1, the surface condition (texture) of the skin in the area targeted for correction will also disappear, resulting in an unnatural image. Put it away. For example, G2 in FIG. 2 shows an example in which the texture of the unique region has disappeared. If the area to be corrected is extended to the periphery, even the shadows around the unique area will disappear, resulting in an even more unnatural image.

Therefore, in this embodiment, first, the analysis unit 109 generates a Y-component image G1 in the unique region and its surrounding area from the original image. Next, as shown in FIG. 2, the Y component image G1 is analyzed, and based on the analysis results, a texture protection map M11 (large), a texture protection map M12 (small), a shadow protection map M21 (large), A shadow protection map M22 (small) is generated. Then, the second image processing unit 110 performs a correction process to make the unique region less noticeable in the original image using the generated texture protection maps M11, M12 and shadow protection maps M21, M22. In FIG. 2, the image after the correction process is indicated by G2. In addition, the analysis unit 109 analyzes the Y component image G1 before correction to obtain information (texture information) indicating the surface condition (texture) of the subject's skin, and the second image processing unit 110 analyzes the image G1. In a section 109, the texture information obtained from the information before correction is added to the image G2 after the correction process, thereby generating a corrected image G3 in which the texture and shadows have not disappeared.

Here, the method for detecting the unique region to be corrected from the original image is not particularly limited, and any method may be used. For example, an area specified by the user using the operation unit 105 from the face area of the original image displayed on the display unit 103 may be detected as the unique area. Alternatively, a face area may be detected from the original image, the image of the detected face area may be cut out, and pixels different from the surrounding area may be extracted from data obtained by masking the eyes, nose, and mouth of the V component image data within the contour area of the face area. It is also possible to extract a region of values (pixel values that occur suddenly) and detect a unique region based on the size of the region.

FIG. 3 is a block diagram showing the functional configuration of the analysis section 109 and the second image processing section 110.
As shown in FIG. 3, the analysis section 109 includes a skin analysis section 20, a protection map data setting section 21, a low frequency data generation section 22, an edge information generation section 23, a region division section 24, a shadow protection map generation section 25, It is configured to include a texture protection map generation section 26. Further, the second image processing section 110 includes a protection map boundary reliability determination section 27, a correction processing section 28, a transparent composition processing section 29, and a composition processing section 30. The processing of the analysis unit 109 and the second image processing unit 110 will be described below with reference to FIG.

First, the analysis unit 109 generates a Y component image composed of Y (luminance) components from an original image having each YUV component, and further places a singular region detected from the face region of the Y component image in the center. A rectangular area of a predetermined size included in the area is cut out and input to the skin analysis section 20 and the edge information generation section 23.

The skin analysis unit 20 analyzes the Y component image within the input rectangular area to detect a pore image area, and calculates pore size data (size) and pore contrast data (con) in this pore image area. get. Then, for the acquired pore size data, the average data (size_ave) of the pore size data and the standard deviation data (size_sd) of the pore size data are calculated and output to the protection map data setting unit 21 and the low frequency data generation unit 22. do. Furthermore, regarding the acquired contrast data of the pore area, average data (con_ave) of the contrast data of the pore area and standard deviation data (con_sd) of the contrast data of the pore area are calculated and output to the texture protection map generation unit 26. .

The protection map data setting unit 21 creates two types of elliptical protection exclusion areas 61 and 62 (see FIG. 2) with different sizes so that when correcting the unique area, other areas are not corrected. Protection maps M1 and M2 consisting of mask data having the following are generated and output to the area dividing section 24 and the texture protection map generation section 26. The specific area is a substantially circular area where pimples such as acne and undulations are noticeable from the surrounding area, and the protection exclusion areas 61 and 62 of the protection maps M1 and M2 have an elliptical shape surrounding the specific area.
For example, the protection map data setting unit 21 first reads out a basic protection map having a basic ellipse-sized protection exclusion area stored in the storage unit 102 . Next, the radius of the pore size is determined based on the sum of the average data (size_ave) of the pore size data and the standard deviation data (size_sd) of the pore size data calculated by the skin analysis unit 20, and the radius is used as the basis. A protection map M1 having a protection exclusion area equal to the radius of the ellipse of the protection map M1, and a protection map M2 having a protection exclusion area having an ellipse size obtained by subtracting the calculated radius from the radius of the ellipse of the basic protection map are generated.
In addition, since there is generally little individual difference in pore size, protection maps M1 and M2 of two types of sizes may be generated in advance and stored in the storage unit 102. Further, in this embodiment, protection maps M1 and M2 of two types of sizes are generated as templates, but the invention is not limited to this, and multiple types may be generated depending on the size. The larger the size, the more accurate correction of the unique region becomes possible.

The low-pass data generation unit 22 uses the Y component image as a parameter, which is the sum of the average data (size_ave) of the pore size data input from the skin analysis unit 20 and the standard deviation data (size_sd) of the pore size data. Edge components in the Y-component image are removed by smoothing using the average filter, and low-frequency data (data in a low frequency band) representing a rough shading state is generated. This allows the influence of pores to be removed from skin shadows. The low frequency data generation section 22 outputs the generated low frequency data to the edge information generation section 23 and the area division section 24.

The edge information generation unit 23 generates difference data in the positive direction (positive value) and difference data in the negative direction (negative value) between the low frequency data generated by the low frequency data generation unit 22 and the Y component image. Generate/obtain. This makes it possible to extract texture in a frequency band higher than that of pores. The edge information generation section 23 outputs the positive direction difference data and the negative direction difference data to the composition processing section 30 as edge information (texture information) corresponding to the texture.

The area dividing unit 24 divides an annular area between the ellipse of the protection map M1 and the ellipse of the protection map M2 input from the protection map data setting unit 21 based on the low frequency data input from the low frequency data generation unit 22. Among them, a shadow area where the original shadow expression is affected, such as the shadow disappearing when correction is applied, is identified, and this area is divided from other areas.

Specifically, the area dividing unit 24 calculates pixel values in an annular area that is the difference in size between the ellipse of the protection map M1 and the ellipse of the protection map M2 when the large and small protection maps M1 and M2 are superimposed on the low frequency data. Calculate the histogram of (pixel values of low frequency data), find the mode (peak) from this histogram, and use the area of pixel values from the found mode as the center to the range where the minimum value exists as a shaded area. Separate from other areas. Then, the information on the shadow area is output to the shadow protection map generation section 25.

The shadow protection map generation unit 25 adds a shadow area to the area to be protected by the protection maps M1 and M2 based on the shadow area information input from the area division unit 24, and creates a shadow protection map as shown in FIG. Protection maps M11 and M12 are generated. As a result, shadows that are outside the specific area but fall into the protection exclusion area when processed using the protection maps M1 and M2, and some or all (in many cases, part) of information are missing, resulting in unnatural shadows. It becomes possible to add the entire area to protection. That is, by having the correction processing unit 28 perform the correction process using the shadow protection maps M11 and M12, it becomes possible to perform the correction process excluding the shadow areas included in the protection exclusion areas 61 and 62. The shadow protection map generation section 25 outputs the generated shadow protection maps M11 and M12 to the correction processing section 28 and the protection map boundary reliability determination section 27.

The texture protection map generation unit 26 adds the average data (con_ave) of the contrast data of the pore area obtained from the skin analysis unit 20 and the standard deviation data (con_sd) of the contrast data of the pore area, and calculates the value of the higher intensity. Pixels having positive edges (edges with higher luminance than the surroundings) and pixels having negative edges (edges with lower luminance than the surroundings) are determined. Next, in each of the protection maps M1 and M2, the pixels of the positive direction edge and the pixel of the negative direction edge located in the pixel region on the boundary with the protection exclusion region are added to the protection target region of the protection maps M1 and M2 to calculate the texture. protection maps M21 and M22 are generated. By doing this, it is possible to exclude from the correction process pixels that have an edge strength greater than the pores that exist on the boundary with the non-protected area of the protection map, and eliminate the unnaturalness that remains in the image after correction. Can be done. The texture protection map generation unit 26 outputs the generated texture protection maps M21 and M22 to the correction processing unit 28 and the protection map boundary reliability determination unit 27.

The protection map boundary reliability determination unit 27 generates a large-sized protection map that is a combination of the two large-sized protection maps M11 and M21 generated in the shadow protection map generation unit 25 and the texture protection map generation unit 26; The reliability of each of the image regions subjected to correction processing is determined using each of the small-sized protection maps that are a combination of the two small-sized protection maps M12 and M22. Then, in transparent synthesis processing to be described later, a map is generated that increases the degree of influence of the more reliable pixels and lowers the degree of influence of the less reliable pixels, and outputs it to the transparent synthesis processing section 29. Note that the reliability of each image area after the correction process may be determined before the correction process.

Here, image areas where the protection target areas of the large protection map and the small protection map overlap, and image areas where the protection exclusion areas of both overlap, can be corrected using either the large or small protection map. Since the processing results are the same, the reliability is the same. Therefore, here, the reliability is mainly determined for each pixel in the image area sandwiched between the boundary with the protection exclusion area of the large protection map and the boundary between the protection exclusion area of the small protection map. In this embodiment, it is determined that the reliability is higher if the area is more likely to be an ellipse. For example, the boundary sharing rate, area sharing rate, boundary regression analysis matching rate, etc. are calculated for each pixel, and the reliability of the large and small protection maps is determined based on the calculation results.

Here, the boundary sharing rate is the length of the boundary with the protection exclusion area in the large and small protection maps, and the boundary with the protection exclusion area in the large and small protection maps (shared). ) is the length ratio. If the protection exclusion area (that is, the correction target area) in the large and small protection maps is an ellipse, it does not share the boundary with the protection exclusion area of the large and small protection maps, so the higher the ratio of the shared portion, the lower the reliability.
The area sharing ratio is the ratio of the area of the protection edge (area added to the protection target area) to the area of the protection map in each of the large-sized protection map and the small-sized protection map. The lower the proportion of protected edges (the lower the sharing rate), the higher the reliability.
Boundary regression analysis concordance rate measures how many boundary values are within the range of regression analysis results when regression analysis is performed on the boundary with the protection exclusion area for both large-sized protection maps and small-sized protection maps. This is the value shown. Boundary regression analysis If the concordance rate is low, lower the confidence level.
Note that the reliability calculation method described above is an example, and the concept and specific method for determining reliability are not limited thereto.

The correction processing unit 28 generates two large-sized protection maps M11, which are generated by the shadow protection map generation unit 25 and the texture protection map generation unit 26, in a rectangular area including the unique area of the image of each YUV component of the original image. A large-sized protection map including M21 is applied to perform a correction process to make the boundary between the image area of the protection exclusion area surrounded by the protection map and the other image area and the peculiar state less noticeable. Similarly, the two small-sized protection maps M12 and M22 generated in the shadow protection map generation unit 25 and the texture protection map generation unit 26 are combined into a rectangular area including the unique area of the image of each YUV component of the original image. A small-sized protection map is applied to perform a correction process to make the boundary between the image area of the protection exclusion area surrounded by the protection map and the other image area and the peculiar state less noticeable.
The above correction processing mainly uses smoothing processing (processing to smooth the skin condition, processing to give transparency), but color correction processing (processing to correct the skin color to a more natural color) is also used. Sometimes used.
The correction processing section 28 outputs each YUV component image that has been corrected using the large-sized protection map and each YUV component image that has been corrected using the small-sized protection map to the transparent composition processing section 29 .

Based on the map output from the protection map boundary reliability determination section 27, the transmission synthesis processing section 29 separates each YUV component image that has been corrected using the large-sized protection map and the YUV component image that has been corrected using the small-sized protection map. Each YUV component image is transparently synthesized by setting an α value for each pixel targeted for correction processing by two protection maps so that the influence of the pixel determined to be higher is higher. Output.

The combination processing unit 30 adds edge information corresponding to the texture input from the edge information generation unit 23 to the correction processing target area of each YUV component image transparently combined by the transmission combination processing unit 29, and adds the state of the texture. The corrected image (image after correction) and the original image are α-blended at a predetermined blending ratio, and a corrected image is output.

By performing the above processing in the analysis unit 109 and the second image processing unit 110, as shown in the corrected image G3 of FIG. Image processing can be performed to make specific areas such as pimples less noticeable.

As described above, according to the imaging device 1, the analysis unit 109 acquires a subject image and acquires surface state information of the subject from the acquired subject image. The second image processing unit 110 processes the subject image based on the surface state information acquired by the analysis unit 109.
Therefore, it is possible to obtain a subject image that maintains the surface condition of the subject based on the surface condition information of the subject. Furthermore, when a subject image is corrected and the surface state of the subject is missing, the corrected location can be made less noticeable based on the surface state information.

Further, for example, the analysis unit 109 acquires edge components in a predetermined frequency band in the image region of the subject image, and acquires the acquired edge components as surface state information.
Therefore, it is possible to obtain a subject image that maintains the surface state of the subject consisting of edges in a predetermined frequency band.

Further, for example, the second image processing unit 110 processes the subject image that has been subjected to predetermined processing based on the acquired surface state information. Therefore, the surface state of the subject can be added to the subject image that has been subjected to predetermined processing.

For example, the second image processing unit 110 performs processing based on the surface state information acquired by the analysis unit 109 when processing to make a unique region in the subject image less noticeable, such as smoothing processing, is performed. give Therefore, even if the surface condition of the object is missing by performing processing to make the unique region less noticeable in the object image, processing based on the surface condition can be performed to make the processed area less noticeable.

For example, the second image processing unit 110 uses protection maps of multiple sizes that imitate the shape surrounding the unique region, and performs predetermined processing on the image region including the unique region surrounded by the protection map. The reliability information of each image area that has been subjected to predetermined processing using each of the protection maps of multiple sizes is acquired, and the predetermined processing using the multiple protection maps is performed based on the obtained reliability information. Synthesize the results by changing the degree of influence. Therefore, the processing result processed using the more reliable protection map can be reflected in the subject image.

Further, for example, the analysis unit 109 analyzes the brightness information of the subject image, and corrects the shapes of the plurality of types of protection maps based on the analysis results. Therefore, it is possible to process a unique region of a subject image using a mask-shaped protection map corresponding to the subject image.

Furthermore, since the analysis unit 109 and the second image processing unit 110 of the imaging device 1 perform the above processing on the subject image including the human skin region, it is possible to obtain a subject image that retains the texture of the person's skin.

In addition, the second image processing unit 110 of the imaging device 1 corrects the unique region in the subject image to make it less noticeable with respect to the surroundings, and applies the surface state of the subject in the subject image before correction to the corrected specific region. Perform additional processing. Therefore, when a correction is performed to make a specific area of a subject image less noticeable, the corrected area can be made less noticeable.

Note that the content described in the above embodiment is a preferred example of the present invention, and the present invention is not limited thereto.

For example, in the above embodiments, the case where the image processing device of the present invention is included in the imaging device has been described as an example, but the image processing device of the present invention may be separate from the imaging device. For example, it may be an image processing device that performs the processing described using FIGS. 2 and 3 on image data received from an imaging device. Further, the original image is not limited to a recorded image, but may be a live view image that is sequentially captured by the imaging unit 106 and sequentially displayed on the display unit 103. By doing so, it is possible to always provide the user with a subject image (face) that reflects the corrected results.

Further, in the above embodiment, the case where YUV image data is used is explained as an example, but the type of image data is not particularly limited.

Further, in the above embodiment, the case where both texture and shadow are protected is explained as an example, but it is also possible to protect only one of them.
When protecting only the texture, multiple texture protection maps with different sizes of protection exclusion areas are generated based on the Y component image of the original image, and each of the multiple texture protection maps is used to protect the YUV component of the original image. Apply correction processing to the image, calculate the reliability of each of the corrected image areas using each of the multiple texture protection maps, calculate the α value for each pixel based on the reliability, and use the multiple texture protection maps to calculate the α value for each pixel. A plurality of images corrected using each protection map are transparently synthesized. Further, the texture information acquired from the Y component image is added to the transparently synthesized image and synthesized with the YUV component image of the original image at a predetermined α value.
When protecting only shadows, multiple shadow protection maps with different sizes of protection exclusion areas are generated based on the Y component image of the original image, and each of the multiple shadow protection maps is used to protect the YUV component of the original image. Apply correction processing to the image, calculate the reliability of each corrected image area using each of the multiple shadow protection maps, calculate the α value for each pixel based on the reliability, and calculate the α value for each pixel based on the reliability. A plurality of images corrected using each protection map are transparently synthesized. The transparently synthesized image is then synthesized with the YUV component image of the original image using a predetermined α value.

Further, for example, in the above description, an example is disclosed in which a hard disk, a semiconductor nonvolatile memory, or the like is used as a computer-readable medium for the program according to the present invention, but the present invention is not limited to this example. As other computer-readable media, it is possible to apply a portable recording medium such as a CD-ROM. Further, a carrier wave is also applied as a medium for providing data of the program according to the present invention via a communication line.

In addition, the detailed configuration and detailed operation of each device constituting the imaging device can be changed as appropriate without departing from the spirit of the invention.

Although several embodiments of the present invention have been described, the scope of the present invention is not limited to the above-described embodiments, but includes the scope of the invention described in the claims and its equivalents.
Below, the invention described in the claims first attached to the application of this application will be added. The claim numbers listed in the supplementary notes are as in the claims originally attached to the request for this application.
[Additional notes]
<Claim 1>
an acquisition means for acquiring a subject image;
Texture information acquisition means for acquiring texture information of the subject from the subject image acquired by the acquisition means;
processing means for processing the subject image based on the texture information acquired by the texture information acquisition means;
An image processing device comprising:
<Claim 2>
comprising edge component acquisition means for acquiring edge components of a predetermined frequency band in the image area of the subject image;
2. The image processing apparatus according to claim 1, wherein the texture information acquisition means acquires an edge component acquired by the edge component acquisition means as the texture information.
<Claim 3>
further comprising processing means for performing predetermined image processing on the subject image,
The image according to claim 1 or 2, wherein the processing means processes the subject image that has been subjected to predetermined processing by the processing means, based on the texture information acquired by the texture information acquisition means. Processing equipment.
<Claim 4>
Further comprising a detection means for detecting a specific region different from the surrounding area in the subject image,
4. The image processing apparatus according to claim 3, wherein the predetermined process is a process for making the unique area in the subject image less noticeable.
<Claim 5>
The processing means includes:
Using mask data of a plurality of sizes imitating the shape surrounding the singular region, performing the predetermined processing on an image region including the peculiar region surrounded by the mask data, respectively;
Reliability information acquisition means for acquiring reliability information for each of the image regions subjected to the predetermined processing using each of the mask data of the plurality of types of sizes;
a synthesizing means for synthesizing the results of predetermined processing in each of the mask data processed by the processing means while changing the degree of influence, based on the reliability information acquired by the reliability information acquisition means;
The image processing apparatus according to claim 4, further comprising: an image processing apparatus according to claim 4;
<Claim 6>
analysis means for analyzing brightness information of the subject image;
correction means for correcting the shapes of the plurality of types of mask data based on the analysis results by the analysis means;
The image processing apparatus according to claim 5, further comprising:
<Claim 7>
The image processing apparatus according to claim 4, wherein the predetermined processing is smoothing processing.
<Claim 8>
The image processing apparatus according to any one of claims 1 to 7, wherein the subject image is an image including a human skin area.
<Claim 9>
an acquisition means for acquiring a subject image;
Detection means for detecting a unique region different from the surrounding area from the subject image;
correction means for correcting the specific region detected by the detection means to make it less noticeable with respect to the surroundings;
A control means for controlling the unique region corrected to be inconspicuous by the correction means to perform processing to add a surface state of the subject in the subject image before correction by the correction means;
An image processing device comprising:
<Claim 10>
an acquisition means for acquiring a subject image;
detection means for detecting a unique region different from the surrounding area in the subject image acquired by the acquisition means;
processing means for applying predetermined processing to image regions including the singular region surrounded by the mask data using mask data of a plurality of sizes imitating shapes surrounding the singular region detected by the detection means;
Reliability information acquisition means for acquiring reliability information for each of the image regions subjected to the predetermined processing using each of the mask data of the plurality of types of sizes;
a synthesizing means for synthesizing the results of predetermined processing in each of the mask data processed by the processing means while changing the degree of influence, based on the reliability information acquired by the reliability information acquisition means;
An image processing device comprising:
<Claim 11>
An image processing method in an image processing device, the method comprising:
a step of acquiring a subject image;
acquiring texture information of the subject from the acquired subject image;
processing the subject image based on the acquired texture information;
An image processing method characterized by comprising:
<Claim 12>
An image processing method in an image processing device, the method comprising:
a step of acquiring a subject image;
detecting a specific region different from its surroundings from the subject image;
correcting the detected specific region to make it less noticeable with respect to the surrounding area;
An image processing method comprising the step of: adding a surface state of a subject in the subject image before the correction to the specific region corrected to make it less noticeable.
<Claim 13>
An image processing method in an image processing device, the method comprising:
a step of acquiring a subject image;
detecting a unique region different from the surrounding area in the acquired subject image;
using mask data of a plurality of sizes imitating a shape surrounding the detected singular region, and performing a predetermined process on each of the image regions including the singular region surrounded by the mask data;
acquiring reliability information for each of the image regions subjected to the predetermined processing using each of the mask data of the plurality of sizes;
a synthesizing means for synthesizing the results of the predetermined processing in the respective mask data with varying degrees of influence based on the acquired reliability information;
An image processing method characterized by comprising:
<Claim 14>
computer,
an acquisition means for acquiring a subject image;
Texture information acquisition means for acquiring texture information of the subject from the subject image acquired by the acquisition means;
processing means for processing the subject image based on the texture information acquired by the texture information acquisition means;
A program to function as
<Claim 15>
computer,
an acquisition means for acquiring a subject image;
detection means for detecting a unique region different from its surroundings from the subject image;
correction means for correcting the specific region detected by the detection means to make it less noticeable with respect to the surroundings;
control means for controlling the unique region corrected by the correction means so as to be inconspicuous so as to be processed to add a surface condition of the subject in the object image before correction by the correction means;
A program to function as
<Claim 16>
computer,
an acquisition means for acquiring a subject image;
detection means for detecting a unique region different from the surroundings in the subject image acquired by the acquisition means;
processing means that uses mask data of a plurality of sizes imitating shapes surrounding the singular region detected by the detection means, and performs predetermined processing on each image region including the singular region surrounded by the mask data;
Reliability information acquisition means for acquiring reliability information for each of the image regions subjected to the predetermined processing using each of the mask data of the plurality of types of sizes;
a synthesizing means for synthesizing the results of predetermined processing in each of the mask data processed by the processing means while changing the degree of influence, based on the reliability information acquired by the reliability information acquisition means;
A program to function as

1 Imaging device 101 Control section 102 Storage section 103 Display section 104 Communication section 105 Operation section 106 Imaging section 106a Imaging lens 107 First image processing section 108 Image memory 109 Analysis section 20 Skin analysis section 21 Protection map data setting section 22 Low range Data generation unit 23 Edge information generation unit 24 Area division unit 25 Shadow protection map generation unit 26 Texture protection map generation unit 27 Protection map boundary reliability determination unit 110 Second image processing unit 28 Correction processing unit 29 Transparency synthesis processing unit 30 Synthesis processing section 111 Lighting control section 112 Light emitting section

Claims (9)

an acquisition means for acquiring a subject image;
edge component acquisition means for acquiring edge components in a predetermined frequency band in the image region of the subject image acquired by the acquisition means;
Texture information acquisition means for acquiring the edge component acquired by the edge component acquisition means as texture information;
Detection means for detecting a unique region different from the surrounding area in the subject image;
processing means that performs a correction process to make the specific area in the subject image less noticeable ;
processing means for processing the subject image subjected to the correction processing by the processing means, based on the texture information acquired by the texture information acquisition means;
Equipped with
The processing means includes:
Using mask data of a plurality of sizes imitating the shape surrounding the singular region, predetermined image processing is performed on the image area including the singular region surrounded by the mask data, and the mask data of the plurality of sizes are obtained. reliability information acquisition means for acquiring reliability information for each of the image regions subjected to the predetermined image processing using each of the above;
compositing means for composing the image regions subjected to the predetermined image processing so that the degree of influence of the image region for which reliability information acquired by the reliability information acquisition means is determined to be higher is higher;
An image processing device comprising: the predetermined image processing is smoothing processing;
The image processing device according to claim 1, characterized in that: an acquisition means for acquiring a subject image;
detection means for detecting a unique region different from the surrounding area in the subject image acquired by the acquisition means;
processing means for applying predetermined processing to image regions including the singular region surrounded by the mask data using mask data of a plurality of sizes imitating shapes surrounding the singular region detected by the detection means;
Reliability information acquisition means for acquiring reliability information for each of the image regions subjected to the predetermined processing using each of the mask data of the plurality of types of sizes;
Synthesis of image regions subjected to the predetermined processing by the processing means so that the degree of influence of the image region determined to have higher reliability information acquired by the reliability information acquisition means is higher. means and
An image processing device comprising : analysis means for analyzing brightness information of the subject image;
correction means for correcting the shapes of the plurality of types of mask data based on the analysis results by the analysis means;
The image processing device according to any one of claims 1 to 3, comprising : The subject image is an image including a human skin area,
The image processing device according to any one of claims 1 to 4 . An image processing method in an image processing device, the method comprising:
an acquisition step of acquiring a subject image;
an edge component acquisition step of acquiring an edge component of a predetermined frequency band in the image region of the subject image acquired in the acquisition step;
a texture information acquisition step of acquiring the edge component acquired in the edge component acquisition step as texture information;
a detection step of detecting a specific region different from the surrounding area in the subject image;
a processing step of performing a correction process to make the specific region in the subject image less noticeable;
a processing step of processing the subject image subjected to the correction processing in the processing step based on the texture information obtained in the texture information obtaining step;
including;
The processing step includes:
Using mask data of a plurality of sizes imitating the shape surrounding the singular region, predetermined image processing is performed on the image area including the singular region surrounded by the mask data, and the mask data of the plurality of sizes are obtained. a reliability information acquisition step of acquiring reliability information for each of the image regions subjected to the predetermined image processing using each of the above;
a compositing step of composing the image regions subjected to the predetermined image processing so that the degree of influence of the image region determined to have higher reliability information acquired in the reliability information acquisition step is higher;
An image processing method characterized by comprising: An image processing method in an image processing device, the method comprising:
a step of acquiring a subject image;
Detecting a unique region different from the surrounding area in the acquired subject image;
using mask data of a plurality of sizes imitating a shape surrounding the detected singular region, and performing a predetermined process on each of the image regions including the singular region surrounded by the mask data;
acquiring reliability information for each of the image regions subjected to the predetermined processing using each of the mask data of the plurality of sizes;
compositing the image regions subjected to the predetermined processing so that the degree of influence of the image region determined to have higher reliability information is higher;
An image processing method characterized by comprising: The computer of the image processing device,
an acquisition means for acquiring a subject image;
edge component acquisition means for acquiring edge components of a predetermined frequency band in the image area of the subject image acquired by the acquisition means;
Texture information acquisition means for acquiring the edge component acquired by the edge component acquisition means as texture information;
detection means for detecting a unique region different from the surrounding area in the subject image;
processing means for performing correction processing to make the specific region in the subject image less noticeable;
processing means for processing the subject image subjected to the correction processing by the processing means, based on the texture information acquired by the texture information acquisition means;
function as
The processing means includes:
Using mask data of a plurality of sizes imitating the shape surrounding the singular region, predetermined image processing is performed on the image area including the singular region surrounded by the mask data, and the mask data of the plurality of sizes are obtained. Reliability information acquisition means for acquiring reliability information for each of the image regions subjected to the predetermined image processing using each of the above;
compositing means for composing the image regions subjected to the predetermined image processing so that the degree of influence of the image region determined to have higher reliability information acquired by the reliability information acquisition means is higher;
A program that functions as The computer of the image processing device,
an acquisition means for acquiring a subject image;
detection means for detecting a unique region different from the surroundings in the subject image acquired by the acquisition means;
processing means that uses mask data of a plurality of sizes imitating shapes surrounding the singular region detected by the detection means, and performs predetermined processing on each image region including the singular region surrounded by the mask data;
Reliability information acquisition means for acquiring reliability information for each of the image regions subjected to the predetermined processing using each of the mask data of the plurality of types of sizes;
Synthesis of image regions subjected to the predetermined processing by the processing means so that the degree of influence of the image region determined to have higher reliability information acquired by the reliability information acquisition means is higher. means,
A program to function as

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