JP2006094000A - Image processing method, image processing apparatus, and image processing program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a technology capable of carrying out proper gradation correction for image data without making noise conspicuous. <P>SOLUTION: An image processing adjustment section 701 includes: a scene discrimination section 710 for discriminating a scene of an image on the basis of image data comprising a plurality of pixel signals; a gradation correction condition determining section 730 for determining a gradation correction condition on the basis of a result of the scene discrimination; a noise evaluation section 750 for evaluating a noise increase amount when the gradation correction condition is applied to the image data; and an image processing execution section 740 acting like a gradation correction section wherein a gradation correction condition amount determining section 733 as a gradation correction condition adjustment section adjusts the determined gradation correction condition on the basis of the evaluated noise increase amount and furthermore uses the adjusted gradation correction condition to apply the gradation correction processing to the image data. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to an image processing method, an image processing apparatus, and an image processing program for performing image processing.

  In recent years, digital still cameras (including those incorporated in devices such as mobile phones and laptop computers, hereinafter abbreviated as DSC (Digital Still Camera)) have become widespread and, like conventional color photographic film systems, A system for outputting as a hard copy image, displaying on a medium such as a CRT (Cathode Ray Tube), or recording on a recording medium such as a CD-R (CD-recordable) is widely known.

In general, in many cases, an image photographed by DSC cannot obtain an optimum image as an image to be viewed as it is due to inadequate exposure adjustment at the time of photographing. In order to solve such problems, various proposals have been made. For example, a method of discriminating an image scene by analyzing photographed image data and automatically performing optimum image processing according to the image scene has been proposed (see, for example, Patent Document 1). In addition, a configuration in which tone correction is performed on image data using an arcuate tone correction curve has been considered (see, for example, Patent Documents 2 and 3).
JP 2000-134467 A JP 2000-196890 A JP 2003-143427 A

  Further, in the configuration for discriminating the scene of the image of the captured image data, a configuration in which appropriate gradation correction is performed on the captured image data according to the determined scene is also conceivable. If the scene of the image can be determined appropriately, for example, if it is an underscene, gradation correction is performed so that the image is brightened so that the main part (for example, the face) of the image has appropriate brightness. On the other hand, in a high-contrast scene such as close-up flash photography, it is common to perform tone correction so as to darken the image. As a gradation correction method at this time, generally, if the gradation correction is to brighten the image, a gradation correction curve that is convex upward is applied to darken the image. For example, a downward convex tone correction curve is applied.

  However, in the configuration in which appropriate gradation correction is performed on the captured image data in accordance with the above-described scene determination result, as a result of studies by the present inventors, an image is brightened so as to have optimum brightness in an underscene. Depending on the type of image, the performance of the captured DSC, and the performance of CCD (Charge-Coupled Devices) or CMOS (Complementary Metal-Oxide Semiconductor) as the image sensor, the noise in the shadow part of the image may be affected by performing tone correction. It has been found that the image may be noticeable and may not be a preferable image.

  An object of the present invention is to perform appropriate gradation correction on image data so that noise is not noticeable.

In order to solve the above-mentioned problem, the invention described in claim 1
A scene discriminating step for discriminating a shooting scene of an image from image data composed of signals of a plurality of pixels;
A gradation correction condition determining step for determining a gradation correction condition based on the result of the scene determination;
Evaluating the amount of noise increase when the gradation correction condition is applied to the image data;
A gradation correction condition adjusting step of adjusting the determined gradation correction condition based on the evaluated noise increase amount;
A gradation correction step of applying a gradation correction process to the image data using the adjusted gradation correction condition;
The image processing method characterized by including.

The invention according to claim 2 is the image processing method according to claim 1,
The scene discrimination step includes
A first occupancy ratio calculating step of dividing the image data into areas each including a combination of predetermined brightness and hue, and calculating a first occupancy ratio indicating a ratio of the entire image data for each of the divided areas. When,
A first index calculating step of calculating the first and second indices by multiplying the first occupancy by different first and second coefficients set in advance, respectively;
The image data is divided into a predetermined area composed of a combination of the distance from the outer edge of the screen and the brightness, and a second occupancy ratio indicating the ratio of the image data to the entire image data is calculated for each of the divided areas. 2 occupancy calculation process;
A second index calculating step of calculating a third index by multiplying the second occupancy by a preset third coefficient;
A third index calculating step of calculating a fourth index by multiplying the average brightness of the skin color at least in the center of the screen of the image data by a preset fourth coefficient;
And determining a shooting scene of the image data based on the calculated first, second, third, and fourth indices.

The invention according to claim 3 is the image processing method according to claim 1 or 2,
The gradation correction condition determination step determines a gradation correction method and a gradation correction amount as gradation correction conditions,
The gradation correction condition adjustment step adjusts the determined gradation correction amount based on the noise increase amount,
In the gradation correction step, gradation correction processing is performed on the image data using the determined gradation correction method and the adjusted gradation correction amount.

The invention according to claim 4
Obtaining second image data with a reduced image size from first image data composed of signals of a plurality of pixels;
A scene discriminating step for discriminating a photographing scene of the image from the second image data;
A gradation correction condition determining step for determining a gradation correction condition based on the result of the scene determination;
Evaluating a noise increase amount when the gradation correction condition is applied to the first image data;
A gradation correction condition adjustment step of adjusting the gradation correction condition based on the noise increase amount;
A gradation correction step of applying a gradation correction process to the first image data using the adjusted gradation correction condition;
The image processing method characterized by including.

The invention according to claim 5 is the image processing method according to claim 4,
The scene discrimination step includes
Dividing the second image data into regions each having a combination of predetermined brightness and hue, and calculating a first occupancy ratio that indicates a proportion of the entire image data for each of the divided regions. Rate calculation process;
A first index calculating step of calculating the first and second indices by multiplying the first occupancy by different first and second coefficients set in advance, respectively;
The image data is divided into a predetermined area composed of a combination of the distance from the outer edge of the screen and the brightness, and a second occupancy ratio indicating the ratio of the image data to the entire image data is calculated for each of the divided areas. 2 occupancy calculation process;
A second index calculating step of calculating a third index by multiplying the second occupancy by a preset third coefficient;
A third index calculating step of calculating a fourth index by multiplying the average brightness of the skin color at least in the center of the screen of the image data by a preset fourth coefficient;
And determining a shooting scene of the second image data based on the calculated first, second, third and fourth indices.

The invention according to claim 6 is the image processing method according to claim 4 or 5,
The gradation correction condition determination step determines a gradation correction method and a gradation correction amount as gradation correction conditions,
The gradation correction condition adjustment step adjusts the gradation correction amount based on the noise increase amount,
In the gradation correction step, gradation correction processing is performed on the first image data by using the determined gradation correction method and the adjusted gradation correction amount.

The invention described in claim 7
A scene discriminating unit for discriminating a shooting scene of an image from image data composed of signals of a plurality of pixels;
A gradation correction condition determination unit that determines gradation correction conditions based on the result of the scene determination;
A noise evaluation unit that evaluates a noise increase amount when the gradation correction condition is applied to the image data;
A gradation correction condition adjusting unit that adjusts the determined gradation correction condition based on the evaluated noise increase amount;
A gradation correction unit that performs a gradation correction process on the image data using the adjusted gradation correction condition;
An image processing apparatus comprising:

The invention according to claim 8 is the image processing apparatus according to claim 7,
The scene discrimination unit
A first occupancy ratio calculation unit that divides the image data into areas each including a combination of predetermined brightness and hue, and calculates a first occupancy ratio indicating a ratio of the entire image data for each of the divided areas. When,
A first index calculation unit for calculating the first and second indices by multiplying the first occupancy by different first and second coefficients set in advance;
The image data is divided into a predetermined area composed of a combination of the distance from the outer edge of the screen and the brightness, and a second occupancy ratio indicating the ratio of the image data to the entire image data is calculated for each of the divided areas. 2 occupancy rate calculator,
A second index calculating unit that calculates a third index by multiplying the second occupancy by a preset third coefficient;
A third index calculating unit that calculates a fourth index by multiplying an average brightness of the skin color at least in the center of the screen of the image data by a preset fourth coefficient;
A scene discrimination execution unit that discriminates a shooting scene of the image data based on the calculated first, second, third, and fourth indices.

The invention according to claim 9 is the image processing apparatus according to claim 7 or 8,
The gradation correction condition determining unit determines a gradation correction method and a gradation correction amount as a gradation correction condition,
The gradation correction condition adjustment unit adjusts the determined gradation correction amount based on the noise increase amount,
The gradation correction unit performs a gradation correction process on the image data using the determined gradation correction method and the adjusted gradation correction amount.

The invention according to claim 10 is:
An image reduction unit for obtaining second image data obtained by reducing the image size from the first image data including signals of a plurality of pixels;
A scene discriminating unit for discriminating a photographing scene of the image from the second image data;
A gradation correction condition determination unit that determines gradation correction conditions based on the result of the scene determination;
A noise evaluation unit that evaluates a noise increase amount when the gradation correction condition is applied to the first image data;
A gradation correction condition adjustment unit that adjusts the gradation correction condition based on the noise increase amount;
A gradation correction unit that performs a gradation correction process on the first image data using the adjusted gradation correction condition;
An image processing apparatus comprising:

The invention according to claim 11 is the image processing apparatus according to claim 10,
The scene discrimination unit
Dividing the second image data into regions each having a combination of predetermined brightness and hue, and calculating a first occupancy ratio that indicates a proportion of the entire image data for each of the divided regions. A rate calculator,
A first index calculating step of calculating the first and second indices by multiplying the first occupancy by different first and second coefficients set in advance, respectively;
The image data is divided into a predetermined area composed of a combination of the distance from the outer edge of the screen and the brightness, and a second occupancy ratio indicating the ratio of the image data to the entire image data is calculated for each of the divided areas. 2 occupancy rate calculator,
A second index calculating unit that calculates a third index by multiplying the second occupancy by a preset third coefficient;
A third index calculating unit that calculates a fourth index by multiplying an average brightness of the skin color at least in the center of the screen of the image data by a preset fourth coefficient;
A scene discrimination execution unit that discriminates a shooting scene of the second image data based on the calculated first, second, third, and fourth indices.

The invention described in claim 12 is the image processing apparatus according to claim 10 or 11,
The gradation correction condition determining unit determines a gradation correction method and a gradation correction amount as a gradation correction condition,
The gradation correction condition adjustment unit adjusts the gradation correction amount based on the noise increase amount,
The gradation correction unit performs gradation correction processing on the first image data using the determined gradation correction method and the adjusted gradation correction amount.

The invention according to claim 13
On the computer,
A scene discrimination function for discriminating a shooting scene of an image from image data composed of signals of a plurality of pixels;
A gradation correction condition determining step for determining a gradation correction condition based on the result of the scene determination;
A function of evaluating the amount of noise increase when the gradation correction condition is applied to the image data;
A gradation correction condition adjustment function for adjusting the determined gradation correction condition based on the evaluated noise increase amount;
A gradation correction function for performing a gradation correction process on the image data using the adjusted gradation correction condition;
Is an image processing program for realizing the above.

The invention described in claim 14 is the image processing program according to claim 13,
The scene discrimination function is
A first occupancy ratio calculation function that divides the image data into areas each including a combination of predetermined brightness and hue, and calculates a first occupancy ratio indicating a ratio of the entire image data for each of the divided areas. When,
A first index calculation function for calculating the first and second indices by multiplying the first occupancy by different first and second coefficients set in advance;
The image data is divided into a predetermined area composed of a combination of the distance from the outer edge of the screen and the brightness, and a second occupancy ratio indicating the ratio of the image data to the entire image data is calculated for each of the divided areas. 2 Occupancy rate calculation function,
A second index calculation function for calculating a third index by multiplying the second occupancy by a preset third coefficient;
A third index calculation function for calculating a fourth index by multiplying the average brightness of the skin color at least in the center of the screen of the image data by a preset fourth coefficient;
And a function of discriminating a photographing scene of the image data based on the calculated first, second, third and fourth indices.

The invention according to claim 15 is the image processing program according to claim 13 or 14,
The gradation correction condition determination function determines a gradation correction method and a gradation correction amount as a gradation correction condition,
The gradation correction condition adjustment function adjusts the determined gradation correction amount based on the noise increase amount,
The gradation correction function performs gradation correction processing on the image data using the determined gradation correction method and the adjusted gradation correction amount.

The invention described in claim 16
On the computer,
A function of acquiring second image data obtained by reducing the image size from the first image data including signals of a plurality of pixels;
A scene discrimination function for discriminating a shooting scene of an image from the second image data;
A gradation correction condition determination function for determining a gradation correction condition based on the result of the scene determination;
A function of evaluating an amount of increase in noise when the gradation correction condition is applied to the first image data;
A gradation correction condition adjustment function for adjusting the gradation correction condition based on the noise increase amount;
A gradation correction function for applying a gradation correction process to the first image data using the adjusted gradation correction condition;
Is an image processing program for realizing the above.

The invention according to claim 17 is the image processing program according to claim 16,
The scene discrimination function is
Dividing the second image data into regions each having a combination of predetermined brightness and hue, and calculating a first occupancy ratio that indicates a proportion of the entire image data for each of the divided regions. Rate calculation function,
A first index calculation function for calculating the first and second indices by multiplying the first occupancy by different first and second coefficients set in advance;
The image data is divided into a predetermined area composed of a combination of the distance from the outer edge of the screen and the brightness, and a second occupancy ratio indicating the ratio of the image data to the entire image data is calculated for each of the divided areas. 2 Occupancy rate calculation function,
A second index calculation function for calculating a third index by multiplying the second occupancy by a preset third coefficient;
A third index calculation function for calculating a fourth index by multiplying the average brightness of the skin color at least in the center of the screen of the image data by a preset fourth coefficient;
And a function of discriminating a shooting scene of the second image data based on the calculated first, second, third and fourth indices.

The invention according to claim 18 is the image processing program according to claim 16 or 17,
The gradation correction condition determination function determines a gradation correction method and a gradation correction amount as a gradation correction condition,
The gradation correction condition adjustment function adjusts the gradation correction amount based on the noise increase amount,
The gradation correction function performs gradation correction processing on the first image data using the determined gradation correction method and the adjusted gradation correction amount.

  According to the first, seventh, or thirteenth aspect of the invention, the tone correction condition is determined based on the scene discrimination result of the image data, and the noise increase amount of the image data subjected to the tone correction using the determined tone correction condition is determined. Since the gradation correction conditions are adjusted based on the image data and the gradation correction is performed on the image data using the adjusted gradation correction conditions, the gradation correction of the image data can be performed appropriately without making noise conspicuous. For example, for image data such as an underscene, even when tone correction is performed so that the image is automatically brightened based on the result of scene determination, adjustment of tone correction conditions based on the amount of increase in noise and tone correction thereof, Appropriate image data can be obtained without making noise conspicuous in the shadow portion.

  According to the invention of claim 2, 8 or 14, the first, second, third and fourth indices that quantitatively indicate the shooting scene of the image data are calculated, and the image data is based on these indices. Therefore, the shooting scene of the image data can be specified accurately.

  According to the invention of claim 3, 9 or 15, the gradation correction method and the amount of gradation correction are determined as the gradation correction condition based on the scene discrimination result of the image data, and the determined gradation correction condition is used. The tone correction amount is adjusted based on the amount of noise increase in the tone-corrected image data, and the tone correction is performed using the tone correction method and the tone correction amount, so that noise becomes conspicuous. The tone of the image data can be corrected more appropriately.

  According to the invention of claim 4, 10 or 16, the gradation correction condition is determined based on the scene discrimination result of the reduced second image data, and the gradation correction is performed using the determined gradation correction condition. The tone correction condition is adjusted based on the noise increase amount of the image data, and the first image data is tone-corrected under the adjusted tone correction condition. Therefore, the first image is appropriately generated without conspicuous noise. The gradation of the image data can be corrected, and the calculation amount and calculation time for scene discrimination can be reduced by using the second image data.

  According to the invention described in claim 5, 11 or 17, the first, second, third and fourth indices that quantitatively indicate the shooting scene of the second image data are calculated, and based on these indices. Since the shooting scene of the second image data is determined, the shooting scene of the second image data can be accurately specified.

  According to the invention described in claim 6, 12 or 18, the gradation correction method and the gradation correction amount thereof are determined as the gradation correction condition based on the scene discrimination result of the second image data, and the determined gradation The tone correction amount is adjusted based on the noise increase amount of the first image data subjected to tone correction under the correction condition, and the first image data is tone corrected using the tone correction method and the tone correction amount. Therefore, the gradation of the first image data can be corrected more appropriately without making noise conspicuous.

  DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiments according to the present invention and modifications thereof will be described in detail below in order with reference to the accompanying drawings. However, the scope of the invention is not limited to the illustrated examples.

  A first embodiment according to the present invention will be described with reference to FIGS.

<Appearance Configuration of Image Processing Apparatus 1>
First, the external configuration of the image processing apparatus 1 according to the present embodiment will be described with reference to FIG. FIG. 1 shows an external configuration of an image processing apparatus 1 according to the present embodiment.

  As shown in FIG. 1, the image processing apparatus 1 is provided with a magazine loading unit 3 for loading a photosensitive material on one side of a housing 2. Inside the housing 2 are provided an exposure processing unit 4 for exposing the photosensitive material, and a print creating unit 5 for developing and drying the exposed photosensitive material to create a print. On the other side surface of the housing 2, a tray 6 for discharging the print created by the print creation unit 5 is provided.

  Further, a CRT 8 as a display device, a film scanner unit 9 that is a device for reading a transparent document, a reflective document input device 10, and an operation unit 11 are provided on the upper part of the housing 2. The CRT 8 constitutes display means for displaying an image of image information to be printed on the screen. Further, the housing 2 includes an image reading unit 14 that can read image information recorded on various digital recording media, and an image writing unit 15 that can write (output) image signals to various digital recording media. Yes. In addition, a control unit 7 that centrally controls these units is provided inside the housing 2.

  The image reading unit 14 includes a PC card adapter 14a and a floppy (registered trademark) disk adapter 14b, and a PC card 13a and a floppy (registered trademark) disk 13b can be inserted therein. The PC card 13a has, for example, a memory in which a plurality of frame image data captured by a digital camera is recorded. For example, a plurality of frame image data captured by a digital camera is recorded on the floppy (registered trademark) disk 13b. As recording media on which frame image data is recorded in addition to the PC card 13a and the floppy (registered trademark) disk 13b, for example, a multimedia card (registered trademark), a memory stick (registered trademark), MD (Mini Disc) data, CD- ROM etc.

  The image writing unit 15 includes a floppy (registered trademark) disk adapter 15a, an MO (Magneto Optical Disc) adapter 15b, and an optical disk adapter 15c. The FD 16a, the MO 16b, and the optical disk 16c can be inserted into the image writing unit 15, respectively. Yes. Examples of the optical disc 16c include a CD-R and a DVD (Digital Versatile Disc) -R.

  In FIG. 1, the operation unit 11, the CRT 8, the film scanner unit 9, the reflection original input device 10, and the image reading unit 14 are integrally provided in the housing 2. One or more may be provided separately.

  Further, in the image processing apparatus 1 shown in FIG. 1, there is exemplified an apparatus that creates a print by exposing to a photosensitive material and developing it, but the print creation system is not limited to this, for example, an inkjet system, an electronic system, etc. A method such as a photographic method, a thermal method, or a sublimation method may be used.

<Configuration of Main Parts of Image Processing Apparatus 1>
FIG. 2 shows a main part configuration of the image processing apparatus 1. As shown in FIG. 2, the image processing apparatus 1 includes a control unit 7, an exposure processing unit 4, a print creation unit 5, a film scanner unit 9, a reflective document input device 10, an image reading unit 14, a communication means (input) 32, The image writing unit 15, data storage unit 71, template storage unit 72, operation unit 11, CRT 8, and communication unit (output) 33 are configured.

  The control unit 7 includes a microcomputer, and includes a storage unit (not shown) such as a ROM (Read Only Memory), a RAM (Random Access Memory) (not shown), and a CPU (Central Processing Unit) (not shown). Various control programs stored in the storage unit are expanded in the RAM, and the operations of the respective units constituting the image processing apparatus 1 are controlled in cooperation with the expanded various control programs and the CPU.

  The control unit 7 includes an image processing unit 70, and based on an input signal (command information) from the operation unit 11, an image signal read from the film scanner unit 9 or the reflective original input device 10, and an image reading unit 14. The read image signal and the image signal input from the external device via the communication unit 32 are subjected to image processing to form exposure image information, which is output to the exposure processing unit 4. Further, the image processing unit 70 performs a conversion process corresponding to the output form on the image signal subjected to the image processing, and outputs the image signal. Output destinations of the image processing unit 70 include the CRT 8, the image writing unit 15, the communication means (output) 33, and the like.

  The exposure processing unit 4 exposes an image to the photosensitive material and outputs the photosensitive material to the print creating unit 5. The print creating unit 5 develops and exposes the exposed photosensitive material to create prints P1, P2, and P3. The print P1 is a service size, high-definition size, panoramic size print, the print P2 is an A4 size print, and the print P3 is a business card size print.

  The film scanner unit 9 reads a frame image recorded on a transparent original such as a developed negative film N or a reversal film imaged by an analog camera, and acquires a digital image signal of the frame image. The reflective original input device 10 reads an image on a print P (photo print, document, various printed materials) by a flat bed scanner, and acquires a digital image signal.

  The image reading unit 14 reads frame image information recorded on the PC card 13 a or the floppy (registered trademark) disk 13 b and transfers the frame image information to the control unit 7. The image reading unit 14 includes, as the image transfer means 30, a PC card adapter 14a, a floppy (registered trademark) disk adapter 14b, and the like. The image reading unit 14 reads frame image information recorded on the PC card 13a inserted into the PC card adapter 14a or the floppy (registered trademark) disk 13b inserted into the floppy (registered trademark) disk adapter 14b. Transfer to the control unit 7. For example, a PC card reader or a PC card slot is used as the PC card adapter 14a.

  The communication means (input) 32 receives an image signal representing a captured image and a print command signal from another computer in the facility where the image processing apparatus 1 is installed, or a remote computer via the Internet or the like.

  The image writing unit 15 includes, as the image conveying unit 31, a floppy (registered trademark) disk adapter 15a, an MO adapter 15b, and an optical disk adapter 15c. In accordance with a write signal input from the control unit 7, the image writing unit 15 includes a floppy (registered trademark) disk 16a inserted into the floppy (registered trademark) disk adapter 15a, an MO 16b inserted into the MO adapter 15b, The image signal generated by the image processing method according to the present invention is written to the optical disk 16c inserted into the optical disk adapter 15c.

  The data storage unit 71 stores and sequentially stores image information and order information corresponding to the image information (information on how many prints are to be created from images of which frames, print size information, and the like).

  The template storage means 72 stores at least one template data for setting a synthesis area and a background image, an illustration image, and the like, which are sample image data corresponding to the sample identification information D1, D2, and D3. A predetermined template is selected from a plurality of templates that are set by the operation of the operator and stored in advance in the template storage means 72, and the frame image information is synthesized by the selected template and designated sample identification information D1, D2, D3 The sample image data selected on the basis of the image data and the image data and / or character data based on the order are combined to create a print based on the specified sample. The synthesis using this template is performed by a well-known chroma key method.

  Note that sample identification information D1, D2, and D3 for specifying a print sample is configured to be input from the operation unit 211. These sample identification information is recorded on a print sample or an order sheet. Therefore, it can be read by reading means such as OCR. Or it can also input by an operator's keyboard operation.

  In this way, sample image data is recorded corresponding to the sample identification information D1 for designating the print sample, the sample identification information D1 for designating the print sample is input, and based on the input sample identification information D1. Select the sample image data, synthesize the selected sample image data with the image data and / or text data based on the order, and create a print based on the specified sample. Can actually place a print order and meet the diverse requirements of a wide range of users.

  Also, the first sample identification information D2 designating the first sample and the image data of the first sample are stored, and the second sample identification information D3 designating the second sample and the second sample The image data is stored, the sample image data selected based on the designated first and second sample identification information D2 and D3, and the image data and / or character data based on the order are synthesized, and according to the designation In order to create a print based on a sample, it is possible to synthesize a wider variety of images, and it is possible to create a print that meets a wider variety of user requirements.

  The operation unit 11 includes information input means 12. The information input unit 12 is configured by a touch panel, for example, and outputs a pressing signal of the information input unit 12 to the control unit 7 as an input signal. Note that the operation unit 11 may be configured to include a keyboard, a mouse, and the like. The CRT 8 displays image information and the like according to a display control signal input from the control unit 7.

  The communication means (output) 33 sends an image signal representing a photographed image after image processing of the present invention and order information attached thereto to other computers in the facility where the image processing apparatus 1 is installed, the Internet Etc. to a distant computer via

  As shown in FIG. 2, the image processing apparatus 1 displays an image input unit that captures image information obtained by dividing and metering images of various digital media and an image original, an image processing unit, and a processed image. Print output, image output means for writing to an image recording medium, and means for transmitting image data and accompanying order information to another computer in the facility or a distant computer via the Internet via a communication line, Prepare.

<Internal Configuration of Image Processing Unit 70>
FIG. 3 shows an internal configuration of the image processing unit 70. As shown in FIG. 3, the image processing unit 70 includes an image adjustment processing unit 701, a film scan data processing unit 702, a reflection original scan data processing unit 703, an image data format decoding processing unit 704, a template processing unit 705, and CRT specific processing. A unit 706, a printer specific processing unit 707, a printer specific processing unit 708, and an image data format creation processing unit 709.

  The film scan data processing unit 702 performs a calibration operation specific to the film scanner unit 9, negative / positive reversal (in the case of a negative document), dust scratch removal, contrast adjustment, granular noise removal, and sharpness for the image data input from the film scanner unit 9. Processing such as conversion enhancement is performed, and processed image data is output to the image adjustment processing unit 701. In addition, the film size, the negative / positive type, information relating to the main subject optically or magnetically recorded on the film, information relating to the photographing conditions (for example, information content described in APS), and the like are also output to the image adjustment processing unit 701. .

  The reflection original scan data processing unit 703 performs a calibration operation unique to the reflection original input device 10, negative / positive reversal (in the case of a negative original), dust flaw removal, contrast adjustment, and noise removal on the image data input from the reflection original input device 10. Then, processing such as sharpening enhancement is performed, and processed image data is output to the image adjustment processing unit 701.

  The image data format decoding processing unit 704 restores the compression code, if necessary, according to the data format of the image data input from the image transfer means 30 and / or the communication means (input) 32, and the color data. Are converted into a data format suitable for computation in the image processing unit 70 and output to the image adjustment processing unit 701. Further, when the size of the output image is specified from any of the operation unit 11, the communication unit (input) 32, and the image transfer unit 30, the image data format decoding processing unit 704 detects the specified information, The image is output to the image adjustment processing unit 701. Information about the size of the output image designated by the image transfer means 30 is embedded in the header information and tag information of the image data acquired by the image transfer means 30.

  The image adjustment processing unit 701 receives from the film scanner unit 9, the reflection original input device 10, the image transfer unit 30, the communication unit (input) 32, and the template processing unit 705 based on the command from the operation unit 11 or the control unit 7. The image data is subjected to first image processing (see FIG. 6), which will be described later, to generate digital image data for image formation optimized for viewing on the output medium, and a CRT specific processing unit 706, printer The data is output to the unique processing unit 707, the printer unique processing unit 708, the image data format creation processing unit 709, and the data storage unit 71.

  In the optimization process, for example, when it is assumed that the image is displayed on a CRT display monitor compliant with the sRGB standard, the optimal color reproduction is performed within the sRGB standard color gamut. If output to silver salt photographic paper is assumed, processing is performed so that optimum color reproduction is obtained within the color gamut of silver salt photographic paper. In addition to the compression of the color gamut, gradation compression from 16 bits to 8 bits, reduction of the number of output pixels, processing corresponding to output characteristics (LUT: LookUp Table) of the output device, and the like are also included. Furthermore, it goes without saying that tone compression processing such as noise suppression, sharpening, gray balance adjustment, saturation adjustment, or dodging processing is performed.

  FIG. 4 shows an internal configuration of the image adjustment processing unit 701. As shown in FIG. 4, the image adjustment processing unit 701 includes a scene determination unit 710, a gradation correction condition determination unit 730, an image processing execution unit 740, and a noise evaluation unit 750. The gradation correction condition determination unit 730 includes a gradation correction method determination unit 731, a gradation correction parameter calculation unit 732, and a gradation correction amount determination unit 733.

  FIG. 5A shows an internal configuration of the scene determination unit 710. As shown in FIG. 5A, the scene determination unit 710 includes a ratio calculation unit 712, an index calculation unit 713, and a scene determination execution unit 714. FIG. 5B shows an internal configuration of the ratio calculation unit 712. As shown in FIG. 5B, the ratio calculation unit 712 includes a color system conversion unit 715, a histogram creation unit 716, and an occupancy rate calculation unit 717.

  The color system conversion unit 715 converts RGB (Red, Green, Blue) values of the captured image data into the HSV color system. The HSV color system is a representation of image data in terms of three elements: Hue, Saturation, and Lightness (Value or Brightness), and is based on the color system proposed by Munsell. It has been devised.

  In this embodiment, “brightness” means “brightness” generally used unless otherwise noted. In the following description, V (0 to 255) of the HSV color system is used as “brightness”, but a unit system representing the brightness of any other color system may be used. At that time, it goes without saying that numerical values such as various coefficients described in the present embodiment are recalculated. In addition, it is assumed that the captured image data in the present embodiment is image data having a person as a main subject.

  The histogram creation unit 716 creates a two-dimensional histogram by dividing the photographed image data into regions composed of a predetermined combination of hue and brightness, and calculating the cumulative number of pixels for each of the divided regions. In addition, the histogram creation unit 716 divides the captured image data into predetermined regions composed of combinations of the distance from the outer edge of the screen of the captured image data and the brightness, and calculates the cumulative number of pixels for each of the divided regions. Thus, a two-dimensional histogram is created. Note that the captured image data is divided into regions including combinations of the distance from the outer edge of the screen of the captured image data, brightness, and hue, and a cumulative pixel count is calculated for each of the divided regions, thereby creating a three-dimensional histogram. You may do it. Hereinafter, a method for creating a two-dimensional histogram will be described.

  FIG. 5C shows the internal configuration of the occupation rate calculation unit 717. As shown in FIG. 5C, the occupation rate calculation unit 717 includes a first occupation rate calculation unit 718 and a second occupation rate calculation unit 719. The first occupancy ratio calculation unit 718 indicates the ratio of the cumulative number of pixels calculated by the histogram creation unit 716 to the total number of pixels (the entire captured image data) for each region divided by the combination of brightness and hue. An occupation ratio of 1 (see Table 1 described later) is calculated. The second occupancy rate calculation unit 719 calculates the total number of pixels (shooted image) of the cumulative number of pixels calculated by the histogram creation unit 716 for each region divided by the combination of the distance from the outer edge of the screen of the shot image data and the brightness. A second occupancy ratio (see Table 2 described later) indicating a ratio in the entire data) is calculated.

  FIG. 5D shows the internal configuration of the index calculation unit 713. The index calculation unit 713 includes a first index calculation unit 720, a second index calculation unit 721, and a third index calculation unit 722 as illustrated in FIG.

  The first index calculation unit 720 uses a first coefficient (for example, by discriminant analysis) that is set in advance (for example, by discriminant analysis) in accordance with the shooting conditions in the first occupancy calculated for each region in the first occupancy calculation unit 718. The first index a1 for specifying the photographic scene is calculated by multiplying the sum by multiplying (see Table 3 described later). Here, the shooting scene indicates a light source condition when shooting a subject, such as direct light, backlight, and strobe light, and an exposure condition such as under shooting. The first index a1 indicates characteristics during strobe shooting such as indoor shooting degree, close-up shooting degree, and face color high brightness, and is for separating an image that should be identified as “strobe” from other shooting scenes. It is.

  When calculating the first index a1, the first index calculation unit 720 uses coefficients of different signs for a predetermined high-brightness skin color hue region and a hue region other than the high-brightness skin color hue region. Here, the predetermined high lightness skin color hue region includes regions 170 to 224 in the lightness value of the HSV color system. Further, the hue area other than the predetermined high brightness skin color hue area includes at least one high brightness area of the blue hue area (hue values 161 to 250) and the green hue area (hue values 40 to 160).

  In addition, the first index calculation unit 720 sets the first occupancy calculated for each region in the first occupancy rate calculation unit 718 in advance according to the shooting conditions (for example, by discriminant analysis). A second index a2 for specifying a shooting scene is calculated by multiplying a coefficient (see Table 4 described later) and taking the sum. The second index a2 is a composite indication of characteristics at the time of backlight shooting, such as outdoor shooting degree, sky blue high brightness, and face color low brightness, and separates an image that should be determined as “backlight” from other shooting scenes. Is for.

  When calculating the second index a2, the first index calculation unit 720 differs between the intermediate brightness area of the flesh color hue area (hue values 0 to 39, 330 to 359) and the brightness area other than the intermediate brightness area. The sign coefficient is used. The intermediate lightness area of the flesh color hue area includes lightness values of 85 to 169. The brightness area other than the intermediate brightness area includes, for example, a shadow area (brightness values 26 to 84).

  The second index calculation unit 721 uses a third coefficient (preliminarily (for example, by discriminant analysis)) that is set in the second occupancy calculated for each region by the second occupancy calculation unit 719 according to the shooting conditions (for example, by discriminant analysis). The third index a3 for specifying the photographic scene is calculated by multiplying the sum by multiplying (see Table 5 described later). The third index a3 indicates the difference in contrast between the center and the outside of the screen of the captured image data between the backlight and the strobe. Only the image that should be identified as the backlight or the strobe is quantitatively expressed. It is shown. When calculating the third index a3, the index calculation unit 713 uses coefficients having different values depending on the distance from the outer edge of the screen of the captured image data.

  The third index calculation unit 722 multiplies the average luminance value of the skin color at the center of the screen of the captured image data by a fourth coefficient set in advance (for example, by discriminant analysis) according to the shooting conditions. By taking the above, the fourth index a4 for specifying the shooting scene is calculated. More preferably, not only the average luminance value of the skin color in the center of the screen of the photographed image data, but also the difference value between the maximum luminance value of the image and the average luminance value (see formula (8) described later), the luminance standard deviation, and the center of the screen A comparison value (see equation (9) described later) between the average brightness value, the difference between the maximum skin color brightness value and the minimum skin color brightness value of the image, and the skin color average brightness value is multiplied by a preset fourth coefficient. As a result, the fourth index a4 for specifying the shooting scene is calculated. Here, it goes without saying that the fourth coefficient is changed depending on the variable used. The fourth index a4 indicates not only the difference in the contrast between the center and the outside of the screen of the captured image data in the strobe shooting scene and the under shooting scene, but also the distribution information in the luminance histogram. Alternatively, only the image that should be identified as an under shooting scene is quantitatively shown.

  When calculating the fourth index a4, the third index calculation unit 722 calculates the average brightness value of the skin color at the center of the screen of the captured image data, the difference value between the maximum brightness value and the average brightness value of the image, the brightness standard deviation, The average value of brightness in the center of the screen, the difference value between the skin color maximum brightness value and the skin color minimum brightness value, and the comparison value of the skin color average brightness value are used. Here, the brightness value is an index representing brightness. It is also possible to use an index (for example, a brightness value of the HSV color system) other than that. Further, as the maximum luminance value, the flesh color maximum luminance value, and the flesh color minimum luminance value, the luminance value of a pixel in which the cumulative number of pixels from the maximum and minimum luminance values reaches a predetermined ratio with respect to all pixels may be used.

  The third index calculation unit 722 multiplies the first index a1 and the third index a3 by a coefficient set in advance (for example, by discriminant analysis) according to the imaging condition, and takes the sum. An index a5 of 5 is calculated. More preferably, the first index a1, the third index a3, and the fourth ′ index a4 ′ (skin color average luminance value in the center of the screen) are each preliminarily set according to the shooting conditions (for example, by discriminant analysis). ) The fifth index a5 may be calculated by multiplying the set coefficient to obtain the sum. Further, the third index calculation unit 722 multiplies the second index a2 and the third index a3 by a coefficient set in advance (for example, by discriminant analysis) according to the shooting conditions, and combines them. A sixth index a6 is calculated. More preferably, the sixth index a6 is synthesized by multiplying the second index a2, the third index a3, and the fourth ′ index a4 ′ by a coefficient set in advance according to the shooting conditions. It may be calculated.

  Based on the values of the fourth index a4, the fifth index a5, and the sixth index a6 calculated by the index calculation unit 713, the scene determination execution unit 714 captures a shooting scene (light source condition and exposure condition) of the captured image data. ).

  Note that a specific calculation method of the first index a1 to the sixth index a6 in the index calculation unit 713 and a shooting scene (light source condition and exposure condition) determination method in the scene determination execution unit 714 will be described later. This will be described in detail in the explanation of the operation of the embodiment.

  In FIG. 4, a gradation correction condition determination unit 730 determines a gradation correction method and a gradation correction amount as a gradation correction condition. The gradation correction method determination unit 731 determines the gradation correction method based on the shooting scene of the captured image data determined by the scene determination unit 710. For example, when the photographic scene is front light, as shown in FIG. 19A described later, there is a method (tone correction method A) for correcting the translation (offset) of the pixel value of the input photographic image data. Applied. When the shooting scene is backlit, as shown in FIG. 19B described later, a method (gradation correction method B) for gamma correction of pixel values of input captured image data is applied. When the shooting scene is a strobe, a method (tone correction method C) for applying gamma correction and translation (offset) correction to the pixel value of the input shot image data is applied as shown in FIG. Is done. When the shooting scene is under, as shown in FIG. 19B, a method (tone correction method B) for applying gamma correction to the pixel value of the input shot image data is applied.

  The gradation correction parameter calculation unit 732 is a parameter (key correction) necessary for gradation correction based on the values of the fourth index a4, the fifth index a5, and the sixth index a6 calculated by the scene determination unit 710. Value).

  The gradation correction amount determination unit 733 provisionally determines the gradation correction amount of the gradation correction method determined by the gradation correction condition determination unit 730 based on the parameter calculated by the gradation correction parameter calculation unit 732, It also functions as a tone correction amount adjustment unit that determines (adjusts) the tone correction amount based on the noise increase amount evaluated by the noise evaluation unit 750.

  The image processing execution unit 760 uses the captured image data based on the gradation correction method determined by the gradation correction condition determination unit 730 and the gradation correction amount provisionally determined or determined by the gradation correction amount determination unit 733. Functions as a tone correction unit that applies tone correction processing to the image, and further performs other image processing.

  The noise evaluation unit 750 performs the gradation correction method determined by the gradation correction method determination unit 731 and the gradation correction amount provisionally determined by the gradation correction amount determination unit 733 for the captured image data before gradation correction. Based on the above, the noise increase amount of the captured image data subjected to the gradation correction by the image processing execution unit 760 is evaluated, and the evaluated noise increase amount is output to the gradation correction amount determination unit 733.

  In FIG. 3, a template processing unit 705 reads predetermined image data (template) from the template storage unit 72 based on a command from the image adjustment processing unit 701, and combines the image data to be processed with the template. Processing is performed, and the image data after the template processing is output to the image adjustment processing unit 701.

  The CRT specific processing unit 706 performs a process such as changing the number of pixels and color matching on the image data input from the image adjustment processing unit 701 as necessary, and combines the information with information that needs to be displayed, such as control information. Image data is output to the CRT 8.

  The printer-specific processing unit 707 performs printer-specific calibration processing, color matching, and pixel number change processing as necessary, and outputs processed image data to the exposure processing unit 4.

  When an external printer 51 such as a large-format ink jet printer can be connected to the image processing apparatus 1 of the present invention, a printer specific processing unit 708 is provided for each printer apparatus to be connected. The printer-specific processing unit 708 performs printer-specific calibration processing, color matching, pixel number change, and the like, and outputs processed image data to the external printer 51.

  The image data format creation processing unit 709 converts the image data input from the image adjustment processing unit 701 into various general-purpose image formats typified by JPEG, TIFF, Exif, and the like as necessary. The completed image data is output to the image transport unit 31 and the communication means (output) 33.

  Note that the film scan data processing unit 702, reflection original scan data processing unit 703, image data format decoding processing unit 704, image adjustment processing unit 701, CRT specific processing unit 706, printer specific processing unit 707, and printer specific shown in FIG. The divisions of the processing unit 708 and the image data format creation processing unit 709 are provided to assist understanding of the functions of the image processing unit 70, and are not necessarily realized as physically independent devices. Alternatively, it may be realized as a type of software processing performed by a single CPU.

  Next, the operation in this embodiment will be described. In the present embodiment, the first image processing for image processing of captured image data is read from a storage unit (not shown) in the image processing unit 70 using the first image processing execution input from the operation unit 11 as a trigger. The program is executed in cooperation with the CPU (not shown) and the image processing program that has been extracted and expanded in the RAM (not shown).

  FIG. 6 shows the flow of image processing. As shown in FIG. 6, first, in the image adjustment processing unit 701, the film scanner unit 9, the reflective original input device 10, the image transfer unit 30, and the communication unit (input) 32, the film scan data processing unit 702, the reflective original scan. Captured image data is acquired via the data processing unit 703 and the image data format decoding processing unit (step S1). The image processing (gradation correction processing) of the present embodiment is particularly preferably performed on image data photographed by DSC. Then, scene determination processing is executed by the scene determination unit 710 (step S2).

  FIG. 7 shows a flow of scene discrimination processing during image processing. As shown in FIG. 7, in the scene determination process in step S <b> 2, the first occupancy ratio calculation unit 718 divides the captured image data into predetermined image areas, and indicates the ratio of each divided area to the entire captured image data. A first occupancy rate calculation process for calculating an occupancy rate of 1 is executed (step S21).

  FIG. 8 shows the flow of the first occupancy rate calculation process during the scene determination process. As shown in FIG. 8, in the first occupancy ratio calculation process in step S21, first, the RGB value of the photographed image data is converted into the HSV color system by the color system conversion unit 715 (step S211). FIG. 9 shows an example of a conversion program (HSV conversion program) that obtains a hue value, a saturation value, and a lightness value by converting from RGB to the HSV color system using program code (C language). In the HSV conversion program shown in FIG. 9, the values of the digital image data as the input image data are defined as InR, InG, and InB, the calculated hue value is defined as OutH, the scale is defined as 0 to 360, and the saturation The value is OutS, the brightness value is OutV, and the unit is defined as 0 to 255.

  Next, the histogram creation unit 716 divides the captured image data into regions composed of combinations of predetermined brightness and hue, and creates a two-dimensional histogram by calculating the cumulative number of pixels for each divided region (step S212). ). Hereinafter, the area division of the captured image data will be described in detail.

Lightness (V) is lightness value 0-25 (v1), 26-50 (v2), 51-84 (v3), 85-169 (v4), 170-199 (v5), 200-224 (v6) , 225 to 255 (v7). Hue (H) is a skin hue hue area (H1 and H2) with a hue value of 0 to 39, 330 to 359, a green hue area (H3) with a hue value of 40 to 160, and a blue hue area with a hue value of 161 to 250 It is divided into four areas (H4) and a red hue area (H5). Note that the red hue region (H5) is not used in the following calculation because it is found that the contribution to the discrimination of the shooting scene is small. The flesh-color hue area is further divided into a flesh-color area (H1) and other areas (H2). Hereinafter, among the flesh-colored hue regions (H = 0 to 39, 330 to 359), the hue ′ (H) that satisfies the following formula (2) is defined as the flesh-colored region (H1), and the region that does not satisfy the formula (2) is ( H2).
10 <saturation (S) <175,
Hue '(H) = Hue (H) + 60 (when 0 ≤ Hue (H) <300),
Hue '(H) = Hue (H)-300 (when 300 ≤ Hue (H) <360),
Luminance Y = InR x 0.30 + InG x 0.59 x InB x 0.11 (1)
As
Hue '(H) / Luminance (Y) <3.0 × (Saturation (S) / 255) +0.7 (2)
Therefore, the number of divided areas of the captured image data is 4 × 7 = 28. In addition, it is also possible to use the brightness (V) in the expressions (1) and (2).

When the two-dimensional histogram is created, the first occupancy calculating unit 718 calculates a first occupancy indicating the ratio of the cumulative number of pixels calculated for each divided region to the total number of pixels (the entire captured image). (Step S213), the first occupancy rate calculation process ends. Assuming that the first occupancy ratio calculated in the divided area composed of the combination of the lightness area vi and the hue area Hj is Rij, the first occupancy ratio in each divided area is expressed as shown in Table 1.

  Next, in FIG. 7, the second occupancy rate calculation unit 719 executes a second occupancy rate calculation process for calculating the second occupancy rate used to calculate the third index a3 (step S22).

  FIG. 10 shows the flow of the second occupancy rate calculation process during the scene determination process. As shown in FIG. 10, in the first occupancy ratio calculation process in step S22, first, the RGB value of the photographed image data is converted into the HSV color system by the color system conversion unit 715 (step S221). Step S221 may be a step common to step S211. Next, the histogram creation unit 716 divides the photographed image data into regions composed of combinations of the distance from the outer edge of the photographed image screen and the brightness, and creates a two-dimensional histogram by calculating the cumulative number of pixels for each divided region. (Step S222). Hereinafter, the area division of the captured image data will be described in detail.

  11A to 11D show four areas n1 to n4 divided according to the distance from the outer edge of the screen of the captured image data. A region n1 shown in FIG. 11A is an outer frame, a region n2 shown in FIG. 11B is a region inside the outer frame, and a region n3 shown in FIG. A region n4 shown in FIG. 11D is an inner region and is a central region of the captured image screen. Further, the lightness is divided into seven regions v1 to v7 as described above. Therefore, when the captured image data is divided into regions composed of combinations of the distance from the outer edge of the captured image screen and the brightness, the number of divided regions is 4 × 7 = 28.

When the two-dimensional histogram is created, the second occupancy ratio calculation unit 719 calculates a second occupancy ratio indicating the ratio of the cumulative number of pixels calculated for each divided region to the total number of pixels (the entire captured image). (Step S223), the second occupancy rate calculation process ends. If the second occupancy calculated in the divided area composed of the combination of the brightness area vi and the screen area nj is Qij, the second occupancy in each divided area is expressed as shown in Table 2.

  Next, in FIG. 7, the first index calculation unit 720 multiplies the first occupancy calculated by the ratio calculation unit 712 by the first and second coefficients set in advance according to the shooting conditions, First and second indices a1 and a2 are calculated as indices for specifying the shooting scene (quantitatively representing the light source condition), and the second index calculation unit 721 calculates the second index calculated by the ratio calculation unit 712. The occupation ratio is multiplied by a third coefficient set in advance according to the shooting conditions, and a third index a3 is calculated as an index for specifying the shooting scene (step S23).

Table 3 shows the first coefficient necessary for calculating the first index a1 that quantitatively indicates the accuracy of strobe shooting, that is, the lightness state of the face area at the time of strobe shooting, for each divided region. The coefficient of each divided area shown in Table 3 is a weighting coefficient that is multiplied by the first occupation ratio Rij of each divided area shown in Table 1.

  FIG. 12 shows a lightness (V) -hue (H) plane. According to Table 3, a positive (+) coefficient is used for the first occupancy calculated from the region (r1) distributed in the high brightness skin color hue region in FIG. 12, and the other hue is blue. A negative (-) coefficient is used for the first occupancy calculated from the hue region (r2). FIG. 13 shows a curve (coefficient curve) in which the first coefficient in the skin color area (H1) and the first coefficient in the other areas (green hue area (H3)) continuously change over the entire brightness. It is shown. According to Table 3 and FIG. 13, in the region of high brightness (V = 170 to 224), the sign of the first coefficient in the skin color region (H1) is positive (+), and other regions (for example, the green hue region) The sign of the first coefficient in (H3)) is negative (-), and it can be seen that the signs of the two are different.

従って、H1〜H4領域の和は、下記の式（３−１）〜（３−４）のように表される。
H1領域の和＝R11×(-44.0)＋R21×(-16.0)＋（中略）...＋R71×(-11.3) …（３−１）
H2領域の和＝R12×0.0＋R22×8.6＋（中略）...＋R72×(-11.1) …（３−２）
H3領域の和＝R13×0.0＋R23×(-6.3)＋（中略）...＋R73×(-10.0) …（３−３）
H4領域の和＝R14×0.0＋R24×(-1.8)＋（中略）...＋R74×(-14.6) …（３−４） When the first coefficient in the lightness region vi and the hue region Hj is Cij, the sum of the Hk regions for calculating the first index a1 is defined as in Expression (3).

Therefore, the sum of the H1 to H4 regions is expressed by the following formulas (3-1) to (3-4).
Sum of H1 regions = R11 x (-44.0) + R21 x (-16.0) + (omitted) ... + R71 x (-11.3) ... (3-1)
Sum of H2 regions = R12 x 0.0 + R22 x 8.6 + (omitted) ... + R72 x (-11.1) (3-2)
H3 region sum = R13 x 0.0 + R23 x (-6.3) + (omitted) ... + R73 x (-10.0) ... (3-3)
Sum of H4 region = R14 x 0.0 + R24 x (-1.8) + (omitted) ... + R74 x (-14.6) ... (3-4)

The first index a1 is defined as in Expression (4) using the sum of the H1 to H4 regions shown in Expressions (3-1) to (3-4).
a1 = sum of H1 region + sum of H2 region + sum of H3 region + sum of H4 region + 4.424 (4)

Table 4 shows the second coefficient necessary for calculating the second index a2 quantitatively indicating the accuracy as backlight photographing, that is, the brightness state of the face region at the time of backlight photographing, for each divided region. The coefficient of each divided area shown in Table 4 is a weighting coefficient by which the first occupation ratio Rij of each divided area shown in Table 1 is multiplied.

  FIG. 14 shows a lightness (V) -hue (H) plane. According to Table 4, a negative (−) coefficient is used for the first occupancy calculated from the region (r4) distributed in the intermediate lightness of the flesh color hue region in FIG. 14, and the low lightness (shadow) of the flesh color hue region is used. ) A positive (+) coefficient is used for the first occupancy calculated from the region (r3). FIG. 15 shows the second coefficient in the skin color area (H1) as a curve (coefficient curve) that continuously changes over the entire brightness. According to Table 4 and FIG. 15, the sign of the second coefficient of the intermediate lightness region of the flesh-colored hue region having the lightness value of 85 to 169 (v4) is negative (−), and the lightness value is 26 to 84 (v2, It can be seen that the sign of the second coefficient in the low brightness (shadow) region of v3) is positive (+), and the sign of the coefficient in both regions is different.

従って、H1〜H4領域の和は、下記の式（５−１）〜（５−４）のように表される。
H1領域の和＝R11×(-27.0)＋R21×4.5＋（中略）...＋R71×(-24.0) …（５−１）
H2領域の和＝R12×0.0＋R22×4.7＋（中略）...＋R72×(-8.5) …（５−２）
H3領域の和＝R13×0.0＋R23×0.0＋（中略）...＋R73×0.0 …（５−３）
H4領域の和＝R14×0.0＋R24×(-5.1)＋（中略）...＋R74×7.2 …（５−４） When the second coefficient in the lightness area vi and the hue area Hj is Dij, the sum of the Hk areas for calculating the second index a2 is defined as in Expression (5).

Accordingly, the sum of the H1 to H4 regions is represented by the following formulas (5-1) to (5-4).
Sum of H1 regions = R11 x (-27.0) + R21 x 4.5 + (omitted) ... + R71 x (-24.0) (5-1)
Sum of H2 area = R12 x 0.0 + R22 x 4.7 + (omitted) ... + R72 x (-8.5) (5-2)
Sum of H3 regions = R13 x 0.0 + R23 x 0.0 + (omitted) ... + R73 x 0.0 (5-3)
H4 area sum = R14 x 0.0 + R24 x (-5.1) + (omitted) ... + R74 x 7.2 (5-4)

The second index a2 is defined as in Expression (6) using the sum of the H1 to H4 regions shown in Expressions (5-1) to (5-4).
a2 = sum of H1 regions + sum of H2 regions + sum of H3 regions + sum of H4 regions + 1.554 (6)
Since the first and second indicators a1 and a2 are calculated based on the distribution amount of the brightness and hue of the photographed image data, they are effective in determining the photographing scene when the photographed image data is a color image.

図１６は、画面領域ｎ１〜ｎ４における第３の係数を、明度全体に渡って連続的に変化する曲線（係数曲線）として示したものである。 Next, Table 5 shows the third coefficient necessary for calculating the third index a3 for each divided region. The coefficient of each divided area shown in Table 5 is a weighting coefficient by which the second occupation rate Qij of each divided area shown in Table 2 is multiplied, and is set in advance according to the shooting conditions.

FIG. 16 shows the third coefficient in the screen areas n1 to n4 as a curve (coefficient curve) that continuously changes over the entire brightness.

従って、n1〜n4領域の和は、下記の式（７−１）〜（７−４）のように表される。
n1領域の和＝Q11×40.1＋Q21×37.0＋（中略）...＋Q71×22.0 …（７−１）
n2領域の和＝Q12×(-14.8)＋Q22×(-10.5)＋（中略）...＋Q72×0.0 …（７−２）
n3領域の和＝Q13×24.6＋Q23×12.1＋（中略）...＋Q73×10.1 …（７−３）
n4領域の和＝Q14×1.5＋Q24×(-32.9)＋（中略）...＋Q74×(-52.2) …（７−４） If the third coefficient in the brightness area vi and the screen area nj is Eij, the sum of the nk area (screen area nk) for calculating the third index a3 is defined as in Expression (7).

Accordingly, the sum of the n1 to n4 regions is represented by the following formulas (7-1) to (7-4).
Sum of n1 regions = Q11 x 40.1 + Q21 x 37.0 + (omitted) ... + Q71 x 22.0 (7-1)
Sum of n2 regions = Q12 x (-14.8) + Q22 x (-10.5) + (omitted) ... + Q72 x 0.0 (7-2)
Sum of n3 regions = Q13 x 24.6 + Q23 x 12.1 + (omitted) ... + Q73 x 10.1 (7-3)
n4 area sum = Q14 x 1.5 + Q24 x (-32.9) + (omitted) ... + Q74 x (-52.2) ... (7-4)

The third index a3 is defined as in Expression (8) using the sum of the n1 to n4 regions shown in Expressions (7-1) to (7-4).
a3 = sum of n1 regions + sum of n2 regions + sum of n3 regions + sum of n4 regions−12.6201 (8)
Since the third index a3 is calculated based on the compositional feature (distance from the outer edge of the screen of the captured image data) based on the brightness distribution position of the captured image data, the captured scene of the monochrome image as well as the color image is captured. It is also effective in determining

Next, in FIG. 7, the third index calculation unit 722 executes a fourth index calculation process for calculating a fourth index a4 as an index for specifying the shooting scene (step S24). FIG. 17 shows the flow of the fourth index calculation process during the scene determination process. In the fourth index calculation process, as shown in FIG. 17, first, the third index calculation unit 722 calculates the luminance Y from the RGB values of the captured image data using the above formula (1), and the captured image data The average luminance value x1 of the skin color at the center of the screen is calculated (step S241). Then, the third index calculation unit 722 calculates a difference value x2 between the maximum luminance value and the average luminance value of the captured image data (step S242). A difference value x2 between the maximum luminance value and the average luminance value is expressed by the following equation (9).
x2 = Ymax−Yave (9)

Then, the luminance standard deviation x3 of the captured image data is calculated by the third index calculation unit 722 (step S243). Then, the third index calculation unit 722 calculates the average brightness value x4 in the center of the screen of the captured image data (step S244). Then, the third index calculation unit 722 calculates a comparison value x5 between the difference value between the flesh color maximum luminance value and the flesh color minimum luminance value of the captured image data and the flesh color average luminance value (step S245). A comparison value x5 between the difference value between the maximum flesh color luminance value and the minimum flesh color luminance value of the image and the flesh color average luminance value is expressed by the following equation (10).
x5 = (Yskin_max−Yskin_min) / 2−Yskin_ave (10)

  Then, the third index calculation unit 722 causes the average luminance value x1 of the skin color in the central portion of the screen, the difference value x2 between the maximum luminance value and the average luminance value of the image, the luminance standard deviation x3, the average luminance value x4 in the central portion of the screen, Each of the comparison value x5 between the difference value between the maximum skin color luminance value and the minimum skin color luminance value of the image and the average skin color luminance value is multiplied by a fourth coefficient set in advance according to the shooting conditions to obtain a sum. Thus, the fourth index a4 is calculated (step S246), and the fourth index calculation process ends.

The fourth index a4 is defined as Equation (11) using a sum obtained by multiplying the value calculated in Steps S241 to S245 by the fourth coefficient obtained by discriminant analysis.
a4 = 0.06 × x1 + 1.13 × x2 + 0.02 × x3 + (− 0.01) × x4 + 0.03 × x5−6.50 (11)
The fourth index a4 has not only the compositional characteristics of the screen of the photographed image data but also the luminance histogram distribution information, and is particularly effective for discriminating the flash photographing scene and the under photographing scene.

  Next, in FIG. 7, the third index calculation unit 722 calculates a fifth index a5 and a sixth index a6 as indices for specifying the shooting scene (step S25).

Here, the average luminance value of the skin color at the center of the screen of the photographed image data is set as a fourth ′ index a4 ′. Here, the center of the screen is, for example, an area composed of the area n2, the area n3, and the area n4 in FIG. The fifth index a5 is defined as in Expression (12) using the first index a1, the third index a3, and the fourth ′ index a4 ′, and the sixth index a6 is the second index Using the a2, the third index a3, and the fourth ′ index a4 ′, it is defined as in the equation (13).
a5 = 0.46 × a1 + 0.61 × a3 + 0.01 × a4′−0.79 (12)
a6 = 0.58 × a2 + 0.18 × a3 + (− 0.03) × a4 ′ + 3.34 (13)
Here, the weighting coefficient by which each index is multiplied in Expressions (12) and (13) is set in advance according to the shooting conditions.

  In FIG. 7, the scene determination execution unit 714 uses the fourth index a4, the fifth index a5, and the sixth index a6 calculated by the index calculation unit 713 to capture a shooting scene of the captured image data. (Light source condition and exposure condition) are discriminated (step S26), and the scene discrimination process is ended. Hereinafter, a method for determining a shooting scene (light source condition and exposure condition) will be described.

  In FIG. 18A, 60 images are shot under each light source condition of forward light, backlight, and strobe, and the fifth index a5 and the sixth index a6 are calculated for a total of 180 digital image data. The values of the fifth index a5 and the sixth index a6 under the conditions are plotted. According to FIG. 18A, when the value of the fifth index a5 is larger than 0.5, there are many flash photography scenes, the value of the fifth index a5 is 0.5 or less, and the sixth index a6 When the value is larger than −0.5, it can be seen that there are many backlight photographing scenes. Thus, the shooting scene (light source condition and exposure condition) can be quantitatively determined based on the values of the fifth index a5 and the sixth index a6.

  Furthermore, the fourth index a4 is added to the fifth index a5 and the sixth index a6 that can discriminate each of the shooting scenes of the front light, the backlight, and the strobe light, so that the three-dimensional shooting scene (light source condition and exposure condition) Can be discriminated, and the discrimination accuracy can be further increased. The shooting scene can be discriminated three-dimensionally, and the discrimination accuracy of the shooting scene can be further improved. The fourth index a4 is particularly effective for discriminating between a flash photography scene in which gradation correction is performed to darken the entire image and an under photography scene in which gradation correction is performed to brighten the entire image.

FIG. 18B illustrates the fourth index a4 and the fifth index of the captured image data in which the fifth index a5 is greater than 0.5 out of the 60 captured image data of the flash shooting scene and the under shooting scene. a5 is calculated and plotted. According to FIG. 18B, it can be seen that when the value of the fourth index a4 is larger than 0, there are many flash shooting scenes, and when the value of the fourth index a4 is 0 or less, there are many under shooting scenes. Table 6 shows the determination contents of the shooting scene based on the values of the fourth index a4 to the sixth index a6.

  In FIG. 6, the gradation correction method determination unit 731 determines a gradation correction method for the captured image data according to the shooting scene determined in step S <b> 2, and the gradation correction parameter calculation unit 732 calculates an index. Based on the index calculated by the unit 713, a parameter (tone correction parameter) necessary for gradation correction is calculated, and the gradation correction amount determination unit 733 determines the gradation based on the calculated gradation correction parameter. A correction amount is provisionally determined (step S3).

  As shown in FIG. 19, the gradation correction method is determined by selecting the gradation correction method A (FIG. 19A) when the shooting scene is a continuous light, and the gradation correction method B when the shooting scene is a backlight. When (FIG. 19B) is selected and the strobe is selected, the gradation correction method C (FIG. 19C) is selected, and when it is under, the gradation correction method B (FIG. 19B) is selected. Selected.

  Hereinafter, the calculation method of the gradation correction parameter calculated in step S3 will be described. In the following description, it is assumed that the 8-bit captured image data is converted in advance to 16 bits, and the unit of the value of the captured image data is 16 bits.

The following parameters P1 to P9 are calculated as parameters necessary for tone correction (tone correction parameters).
P1: Average brightness of the entire shooting screen
P2: Block division average brightness
P3: Average brightness of skin tone area (H1)
P4: First brightness correction value = P1-P2
P5: Reproduction target correction value = Brightness reproduction target value (30360)-P4
P6: First offset value = P5-P1
P7: First key correction value
P7 ': Second key correction value
P8: Second brightness correction value
P9: Second offset value = P5-P8-P1

  Here, a method for calculating the parameter P2 will be described with reference to FIGS. First, a CDF (cumulative density function) is created in order to normalize captured image data. Next, the maximum value and the minimum value are determined from the obtained CDF. The maximum value and the minimum value are obtained for each RGB. Here, the obtained maximum and minimum values for each RGB are Rmax, Rmin, Gmax, Gmin, Bmax, and Bmin, respectively.

Next, normalized image data for any pixel (Rx, Gx, Bx) of the captured image data is calculated. R _point normalization data in R plane is R _point , Gx normalization data in G plane is G _point , Bx normalization data in B plane is B _point , normalization data R _point , G _point , B _point are Are expressed as in the equations (14) to (16), respectively.
R _point = {(Rx−Rmin) / (Rmax−Rmin)} × 65535 (14)
G _point = {(Gx−Gmin) / (Gmax−Gmin)} × 65535 (15)
B _point = {(Bx−Bmin) / (Bmax−Bmin)} × 65535 (16)
Next, the luminance N _point of the pixel (Rx, Gx, Bx) is calculated by Expression (17).
N _point = (B _point + G _point + R _point ) / 3 (17)

FIG. 20A shows a frequency distribution (histogram) of luminance of RGB pixels before normalization. In FIG. 20A, the horizontal axis represents luminance, and the vertical axis represents pixel frequency. This histogram is created for each RGB. When the luminance histogram is created, normalization is performed for each plane with respect to the captured image data according to equations (14) to (16). FIG. 20B shows a histogram of luminance calculated by the equation (17). Since the captured image data is normalized by 65535, each pixel takes an arbitrary value between the maximum value 65535 and the minimum value 0.

  When the luminance histogram shown in FIG. 20B is divided into blocks divided by a predetermined range, a frequency distribution as shown in FIG. 20C is obtained. In FIG. 20C, the horizontal axis represents the block number (luminance) and the vertical axis represents the frequency.

  Next, a process of deleting highlight and shadow areas is performed from the luminance histogram shown in FIG. This is because the average brightness is very high in white walls and snow scenes, and the average brightness is very low in dark scenes, so highlights and shadow areas adversely affect average brightness control. . Therefore, by limiting the highlight area and the shadow area of the luminance histogram shown in FIG. 20C, the influence of both areas is reduced. When the high luminance region (highlight region) and the low luminance region (shadow region) are deleted from the luminance histogram shown in FIG. 21A (or FIG. 20C), the result is as shown in FIG.

  Next, as shown in FIG. 21C, an area having a frequency greater than a predetermined threshold is deleted from the luminance histogram. This is because if there is a part having an extremely high frequency, the data in this part strongly affects the average luminance of the entire captured image, and thus erroneous correction is likely to occur. Therefore, as shown in FIG. 21C, the number of pixels equal to or larger than the threshold is limited in the luminance histogram. FIG. 21D is a luminance histogram after the pixel number limiting process is performed.

  Each block number of the luminance histogram (FIG. 21 (d)) obtained by deleting the high luminance region and the low luminance region from the normalized luminance histogram and further limiting the number of accumulated pixels, and the respective frequencies The parameter P2 is obtained by calculating the average luminance value based on the above.

The parameter P1 is an average value of the brightness of the entire captured image data, and the parameter P3 is an average value of the brightness of the skin color area (H1) in the captured image data. The first key correction value for parameter P7, the second key correction value for parameter P7 ′, and the second luminance correction value for parameter P8 are defined as shown in equations (18) to (20), respectively.
P7 (first key correction value) = {P3 − ((a6 / 6) × 18000 + 22000)} / 24.78 (18)
P7 ′ (second key correction value) = {P3 − ((a4 / 6) × 10000 + 30000)} / 24.78 (19)
P8 (second luminance correction value) = (a5 / 6) × 17500 (20)

  In the provisional determination of the gradation correction amount for the captured image data based on the calculated gradation correction parameter, specifically, a plurality of gradation correction curves set in advance corresponding to the determined gradation correction method are used. A gradation correction curve corresponding to the calculated gradation correction parameter is selected (determined) from among them. Note that a tone correction curve (tone correction amount) may be calculated based on the calculated tone correction parameter.

Hereinafter, a method for determining a gradation correction curve for each shooting scene (light source condition and exposure condition) will be described.
<For direct light>
When the shooting scene is front light, offset correction (parallel shift of 8-bit value) for matching the parameter P1 with P5 is performed by the following equation (21).
RGB value of output image = RGB value of input image + P6 (21)
Therefore, when the photographic scene is front light, a gradation correction curve corresponding to Expression (19) is selected from a plurality of gradation correction curves shown in FIG. Alternatively, the gradation correction curve may be calculated (determined) based on the equation (21).

<In the case of backlight>
When the shooting scene is backlit, a gradation correction curve corresponding to the parameter P7 (first key correction value) shown in Expression (18) is selected from the plurality of gradation correction curves shown in FIG. To do. A specific example of the gradation correction curve of FIG. 19B is shown in FIG. The correspondence relationship between the value of parameter P7 and the selected gradation correction curve is shown below.
If -0.5 <P7 <+ 0.5 → L3
When + 0.5 ≦ P7 <+1.5 → L4
+ 1.5 ≦ P7 <+2.5 → L5
When -1.5 <P7≤-0.5 → L2
When -2.5 <P7≤-1.5 → L1
When the shooting scene is backlit, it is preferable to perform a dodging process together with the gradation correction process. In this case, it is desirable that the degree of the dodging process be adjusted according to the fifth index a5 indicating the backlight intensity.

<In case of under>
When the shooting scene is under, a gradation correction curve corresponding to the parameter P7 ′ (second key correction value) shown in Expression (19) is selected from the plurality of gradation correction curves shown in FIG. Selected. Specifically, the gradation correction curve corresponding to the value of the parameter P7 ′ is selected from the gradation correction curves shown in FIG. 22 in the same manner as the selection method of the gradation correction curve when the shooting scene is backlit. The When the shooting scene is under, the dodging process as shown in the case of backlight is not performed.

<In the case of strobe>
When the shooting scene is a strobe, offset correction (parallel shift of 8-bit value) is performed by the following equation (22).
RGB value of output image = RGB value of input image + P9 (22)
Therefore, when the shooting scene is a strobe, a gradation correction curve corresponding to the equation (22) is selected from a plurality of gradation correction curves shown in FIG. Alternatively, the gradation correction curve may be calculated (determined) based on Expression (22). When the value of the parameter P9 in the equation (22) exceeds a preset predetermined value α, it corresponds to the second key correction value = P9−α from the curves L1 to L5 shown in FIG. Select a curve.

  In the present embodiment, when the gradation correction processing is actually performed on the captured image data, each of the image processing conditions described above is changed from 16 bits to 8 bits.

  Note that if the tone correction method differs greatly between the following light, backlight, and strobe, there is a concern about the effect on image quality when shooting scenes are misidentified.Therefore, there is a tone correction method between the following light, backlight, and strobe. It is desirable to set an intermediate area that moves slowly.

  As described above, according to steps S2 and S3, an index that quantitatively indicates the shooting scene of the shot image data is calculated, the shooting scene is determined based on the calculated index, and the shot image is determined according to the determination result. A gradation correction method for data is determined, a gradation correction amount of the captured image data is provisionally determined, a gradation correction method for appropriately correcting the brightness of the subject, and a provisional determination of the gradation correction amount are performed. It becomes possible.

In particular, in addition to the first index a1 that quantitatively indicates the accuracy of flash photography and the second index a2 that quantitatively shows the accuracy of backlight photography, it is derived from the compositional elements of the captured image data. By determining the shooting scene using the third index a3, it is possible to improve the shooting scene determination accuracy.
Further, by using a fourth index a4 calculated from compositional elements and histogram distribution information, a strobe scene that performs gradation correction that darkens the entire image, and an under shooting scene that performs gradation correction that brightens the entire image, and Can be discriminated, and the discrimination accuracy of the shooting scene can be improved.

  In FIG. 6, the image processing execution unit 740 uses the gradation correction method determined in step S3 and the temporarily determined gradation correction amount to perform gradation correction (provisional gradation correction) on the captured image data. ) Processing is performed (step S4). Then, the noise evaluation unit 750 evaluates the noise increase amount of the captured image data subjected to the temporary gradation correction with respect to the captured image data before the temporary gradation correction (step S5). Here, a known method can be used for evaluating the noise increase amount. For example, a method for evaluating the amount of increase in noise using binomial wavelet transform as described in JP-A-2004-127064 can be used. Here, the wavelet transform will be briefly described.

<Overview of wavelet transform>
Wavelet transform is a multi-resolution transform that decomposes an input signal into a low-frequency band component signal and a high-frequency band component signal by a single conversion operation, and performs the same conversion on the obtained low-frequency band component signal. An operation is performed to obtain a multi-resolution signal composed of a plurality of signals having different frequency bands. When the obtained multiresolution signal is subjected to inverse multiresolution conversion without being processed, the original signal is reconstructed. For example, G. Strang and T. Nguyen “Wavelet and Filter Banks” Wellesley-Cambridge Press (Japanese translation by G. Strang and T. Nguyen “Wavelet Analysis and Filter Bank” Bafukan) provides a detailed explanation. ing.

The wavelet transform uses a wavelet function (formula (23)) that oscillates in a finite range as illustrated in FIG. 23, and converts wavelet transform coefficients <f, ψ _{a, b} > for the input signal f (x), This is a transformation that is decomposed into the sum of the wavelet functions represented by equation (25) by obtaining as in equation (24).

In Expressions (23) to (25), a represents the scale of the wavelet function, and b represents the position of the wavelet function. As illustrated in FIG. 23, the larger the value of the scale a, the smaller the frequency of the wavelet function ψ _{a, b} (x), and the position at which the wavelet function ψ _{a, b} (x) oscillates moves according to the value of the position b. To do. Therefore, equation (25) indicates that the input signal f (x) is decomposed into a sum of wavelet functions ψ _{a, b} (x) having various scales and positions.

<Binary wavelet transform>
Next, binary wavelet transformation, which is one of wavelet transformations, will be described. The binomial wavelet transform is described in S. Mallat, WL Hwang, “Singularity detection and processing with the wavelets” IEEE Trans. Inform. Theory, 1992, 38, p. 617, S.Mallat, S.Zhong, "Characterization of signals from multiscale edges" IEEE Trans. Pattern Anal. Machine Intel. 14 710 (1992), S. Mallat, "A wavelet tour of signal processing 2ed." Academic Press There is a detailed explanation.

但し、ｉは自然数である。式（２６）より、二項ウェーブレット変換では、ウェーブレット関数のスケールａが、２のｉ乗で離散的に定義されている。このｉは、「レベル」と呼ばれる。 The wavelet function used in the binomial wavelet transform is defined as shown in Equation (26).

However, i is a natural number. From the equation (26), in the binomial wavelet transform, the scale a of the wavelet function is discretely defined by 2 to the power of i. This i is called “level”.

ここで、式（２７）の右辺第２項は、レベル１のウェーブレット関数ψ_1,j（ｘ）の総和で表せない残差の低周波帯域成分を、レベル１のスケーリング関数ψ_1,j（ｘ）の総和で表したものである。スケーリング関数はウェーブレット関数に対応して適切なものが用いられる（段落［０１４５］に記載の文献を参照）。式（２７）に示すレベル１のウェーブレット変換によって、入力信号ｆ（ｘ）＝Ｓ₀は、レベル１の高周波帯域成分Ｗ₁と低周波帯域成分Ｓ₁に信号分解されることになる。 When the input signal f (x) is expressed by using the wavelet function ψ _{i, j} (x) of the equation (26), the following equation (27) is obtained.

Here, the second term on the right side of the equation (27) represents the low frequency band component of the residual that cannot be represented by the sum of the level 1 wavelet functions ψ _{1, j} (x) as the level 1 scaling function ψ _{1, j} ( This is expressed as the sum of x). An appropriate scaling function is used corresponding to the wavelet function (see the document described in paragraph [0145]). The input signal f (x) = S ₀ is decomposed into a high frequency band component W ₁ and a low frequency band component S ₁ of level 1 by the level 1 wavelet transform shown in the equation (27).

The low frequency band component S _{i-1 at} level i-1 is signal-decomposed into a high frequency band component W _{i at} level _i and a low frequency band component S _i as shown in equation (28).

  The wavelet function of the binomial wavelet transform has the following characteristics because the minimum moving unit of the position b is constant regardless of the level i as shown in the equation (26).

As a first feature of the binomial wavelet transform, each signal amount of the high frequency band component W _i and the low frequency band component S _i generated by the one-level binomial wavelet transform shown in Expression (28) This is the same as the signal S _i-1 .

従って、二項ウェーブレット変換で生成される高周波帯域成分Ｗ_iは、低周波帯域成分Ｓ_iの１階微分（勾配）で表される。 As a second feature of the binomial wavelet transform, the following relational expression (29) is established between the scaling function φ _{i, j} (x) and the wavelet function ψ _{i, j} (x).

Therefore, the high frequency band component W _i generated by the binomial wavelet transform is represented by the first derivative (gradient) of the low frequency band component S _i .

As a third feature of the binomial wavelet transform, W _i · γ _i (which is obtained by multiplying a high frequency band component by a coefficient γ _i (see the above-mentioned reference on the binomial wavelet) determined according to the level i of the wavelet transform. Hereinafter, this is referred to as a corrected high-frequency band component), depending on the singularity of the signal change of the input signal, between the signal intensity levels of the corrected high-frequency band component W _i · γ _i after the conversion. The relationship follows a certain law.

FIG. 24 shows the waveform of the input signal S _{0 and} the waveform of the corrected high-frequency band component at each level obtained by the wavelet transform. In FIG. 24, (a) shows the input signal S ₀ , (b) shows the corrected high frequency band component W ₁ · γ ₁ obtained by the level 1 binomial wavelet transform, and (c) shows the level 2 binary signal. The corrected high frequency band component W ₂ · γ ₂ obtained by the term wavelet transform is shown, (d) shows the corrected high frequency band component W ₃ · γ ₃ obtained by the level 3 binomial wavelet transform, and (e) is The corrected high frequency band component W · γ ₄ obtained by the level 4 binomial wavelet transform is shown.

Looking at the change in signal intensity at each level, in (a), the corrected high-frequency band component W _i · γ _i corresponding to the gentle (differentiable) signal change indicated by “1” or “4” is ( As shown in (b) → (e), the signal intensity increases as the number of levels i increases.

In the input signal S ₀ , the corrected high frequency band component W _i · γ _i corresponding to the step-like signal change indicated by “2” has a constant signal intensity regardless of the number of levels i. In the input signal S ₀ , the corrected high frequency band component W _i · γ _i corresponding to the signal change of the δ function indicated by “3” increases the number of levels i as shown in (b) → (e). As the signal intensity decreases.

  As a fourth feature of the binomial wavelet transform, a one-level binomial wavelet transform method for a two-dimensional signal such as an image signal is performed by the method shown in FIG. 25 below.

As shown in FIG. 25, the 1-level dyadic wavelet transform, the input signal S _n-1, by treatment with x-direction low pass filter LPFx and y directions of the low-pass filter LPFy, a low frequency band component S _n can get. Further, by processing the input signal S _n-1 with the high-pass filter HPFx in the x direction, a high frequency band component Wx _n is obtained. Furthermore, another high frequency band component Wy _n can be obtained by processing the input signal S _n-1 by the high-pass filter HPFy in the y direction.

As described above, the input signal S _n−1 is decomposed into two high-frequency band components Wx _n and Wy _n and one low-frequency band component S _n by one-level binomial wavelet transform. Two high frequency band components Wx _n, Wy _n correspond to x and y components of the change vector V _n in the two-dimensional low frequency band component S _n. Deflection angle A _n the size M _n of change vector V _n is given by equation (28) and (29).

The two high frequency band components Wx _n obtained in dyadic wavelet transform, the Wy _n and one low frequency band component S _n, by applying a Dyadic Wavelet inverse transform shown in Figure 26, the pre-conversion signal S _{n -1} can be reconstructed. In other words, obtained by treating the S _n and a signal obtained by processing in the low pass filter LPFy pass filter LPFx and y direction of the x-direction, the Wx _n in x direction of the high-pass filter HPFx and y directions of the low-pass filter LPFy And a signal S _n−1 before binomial wavelet transform is obtained by adding the signal obtained by processing Wy _n with the low-pass filter LPFx in the x direction and the high-pass filter HPFy in the y direction. Can do.

  Specifically, the evaluation of the noise increase amount using the binomial wavelet transform described above is performed by first applying the two-level binomial wavelet transform to the captured image data after the provisional gradation correction to obtain the first-level high-frequency. The signal and the second level high frequency signal are compared. A pixel having a second level signal strength lower than the first level signal strength is identified as a noise pixel. That is, the case “3” in FIG. 24 is specified as a noise pixel. Then, the noise level of the identified noise pixel is compared by comparing the first level high frequency signal intensity after provisional tone correction with the first level high frequency signal intensity obtained by performing binomial wavelet transform on the captured image data. The amount of increase can be evaluated.

  In FIG. 6, the gradation correction amount determination unit 733 determines whether or not the noise increase amount evaluated in step S <b> 5 is large (whether or not it is equal to or larger than a predetermined threshold value set in advance) (step S <b> 5). S6).

  When the amount of increase in noise is equal to or greater than the threshold (step S6; YES), the gradation correction amount determination unit 733 corrects the gradation correction amount temporarily determined in step S3 based on the amount of increase in noise. The corrected tone correction amount is determined to be formal (step S7). Specifically, the gradation correction amount is decreased.

  Then, the image processing execution unit 760 performs gradation correction processing on the captured image data based on the determined gradation correction method and the gradation correction amount (step S8). If the amount of increase in noise is less than the threshold value (step S6; NO), the gradation correction amount provisionally determined in step S3 is determined as a formal one, and the process proceeds to step S8. Then, the image processing execution unit 760 performs image processing other than the gradation correction on the image data after the gradation correction (step S9), and the image processing ends.

  The image data subjected to the image processing is sent to the CRT 8, the exposure processing unit 4, the external printer 51, and the image transport unit 31 via the CRT specific processing unit 706, the print specific processing units 707 and 708, and the image data format creation processing unit 709. , And output to the communication means (output) 33.

  As described above, according to the present embodiment, the gradation correction method is determined based on the scene discrimination result of the captured image data, the gradation correction amount is provisionally determined, and the gradation correction method and the provisionally determined gradation correction are determined. The gradation correction amount is determined (adjusted) based on the noise increase amount of the captured image data subjected to the gradation correction based on the amount, and the image data is subjected to gradation correction with the determined gradation correction method and the gradation correction amount. Therefore, it is possible to properly correct the gradation of the captured image data without making noise conspicuous. For example, even when tone correction is performed on captured image data such as an underscene so that the image is automatically brightened according to the result of scene discrimination, adjustment of tone correction conditions based on the amount of noise increase and tone correction thereof Appropriate photographed image data can be obtained without making noise conspicuous in the shadow portion.

  Also, first, second, third, and fourth indices a1, a2, a3, and a4 that quantitatively indicate the photographing scene of the photographed image data are calculated, and the photographing scene of the photographed image data is calculated based on these indices. Therefore, the shooting scene of the shot image data can be specified accurately.

(Modification)
A modification of the above embodiment will be described. FIG. 27 shows an internal configuration of the image adjustment processing unit 701A. The apparatus configuration of this modification is obtained by replacing the image adjustment processing unit 701 in the image processing apparatus 1 of the first embodiment with an image adjustment processing unit 701A shown in FIG. The image adjustment processing unit 701A includes an image reduction unit 760, a scene determination unit 710A, a gradation correction condition determination unit 730A, an image processing execution unit 740A, and a noise evaluation unit 750A. The gradation correction condition determination unit 730A includes a gradation correction method determination unit 731A, a gradation correction parameter calculation unit 732A, and a gradation correction amount determination unit 733A.

  The image reduction unit 760 reduces the captured image data as the first image data (original image data) and converts it into reduced image data as the second image data. As a method for reducing the image size, a known method can be used. Known image size reduction methods include a bilinear method, a bicubic method, and a nearest naver method. The reduction ratio is not particularly limited, but is preferably about 1/2 to 1/10 from the viewpoint of processing speed and the accuracy of the scene discrimination process.

  The scene determination unit 710A and the gradation correction condition determination unit 730A are the same as the scene determination unit 710 and the gradation correction condition determination unit 730 of the first embodiment, but are not captured image data but an image reduction unit 760. Various processes are performed on the output reduced image data. In addition, the image processing execution unit 760A performs gradation correction processing on the captured image data based on the gradation correction method determined corresponding to the reduced image data and the temporarily determined gradation correction amount. The noise evaluation unit 750A performs noise evaluation of the captured image data whose gradation has been corrected. Further, the image processing execution unit 760A performs gradation correction processing on the captured image data based on the gradation correction amount determined by the gradation correction amount determination unit 733A corresponding to the noise evaluation result and the gradation correction method. And other image processing. In the present modification, in the image processing of FIG. 6, the captured image data is reduced to reduced image data by the image reduction unit 760 after step S1, and the reduced image data is processed in step S3. In steps S4 to S9, various processes are performed on the captured image data.

  As described above, according to the present modification, in addition to the effects of the above-described embodiment, the scene discrimination calculation amount and calculation time can be reduced by performing the scene discrimination process using the reduced image data.

  Note that the descriptions in the above-described embodiment and the modifications thereof show examples of the image processing method, the image processing apparatus, and the image processing program according to the present invention, but the present invention is not limited to this. Changes can be made as appropriate without departing from the spirit of the invention.

  For example, in FIGS. 19A, 19B, and 22, the tone correction curves in which the start point and end point (convergence point) of the tone correction curve are set to the min value and the max value of the image data value, respectively. However, the present invention is not limited to this. For example, as shown in FIG. 28, a gradation correction curve may be used in which the start point and end point of the gradation correction curve are set outside the min value and the max value of the image data value, respectively. In this configuration, it is possible to prevent an increase in the number of pixels near the min value and the max value due to the gradation correction.

  In the scene determination process in the above embodiment, the third index a3 may be calculated by multiplying the average luminance of the skin color at least in the center of the screen of the captured image data by the third coefficient. Similarly, in the scene determination process, a new index for determining a shooting scene is calculated based on the first to third indexes a1 to a3, and the scene is determined based on the new index. Good.

1 is a perspective view illustrating an external configuration of an image processing apparatus 1 according to an embodiment of the present invention. 2 is a block diagram showing an internal configuration of the image processing apparatus 1. FIG. 2 is a block diagram illustrating a main configuration of an image processing unit 70 of the image processing apparatus 1. FIG. 3 is a block diagram illustrating an internal configuration of an image adjustment processing unit 701. FIG. The internal configuration (a) of the scene determination unit 710, the internal configuration (b) of the ratio calculation unit 712, the internal configuration (c) of the occupation rate calculation unit 717, and the internal configuration (d) of the index calculation unit 713. FIG. 6 is a flowchart illustrating image processing executed in an image adjustment processing unit 701. It is a flowchart which shows the scene discrimination | determination process in image processing. It is a flowchart which shows the 1st occupation rate calculation process in a scene discrimination | determination process. It is a figure which shows an example of the program converted from RGB to HSV color system. It is a flowchart which shows the 2nd occupation rate calculation process in a scene discrimination | determination process. It is a figure which shows the area | regions n1-n4 determined according to the distance from the outer edge of the screen of picked-up image data. It is a figure which shows the brightness | luminance (V) -hue (H) plane, and the area | region r1 and the area | region r2 on a VH plane. It is a figure which shows the curve showing the 1st coefficient by which the 1st occupation rate for calculating the 1st parameter | index a1 is multiplied. It is a figure which shows the brightness | luminance (V) -hue (H) plane, and the area | region r3 and the area | region r4 on a VH plane. It is a figure which shows the curve showing the 2nd coefficient for multiplying the 1st occupation rate for calculating 2nd parameter | index a2. It is a figure which shows the curve showing the 3rd coefficient by which a 2nd occupation rate is multiplied for calculating 3rd parameter | index a3 according to area | region (n1-n4). It is a flowchart which shows the 4th parameter | index calculation process in a scene discrimination | determination process. A plot (a) of values of the fifth index a5 and the sixth index a6 under each light source condition, and the fourth index a4 and the fifth index a5 of an image in which the fifth index a5 is greater than 0.5. It is a figure which shows the plot (b). It is a figure which shows the gradation correction curve corresponding to each gradation correction method. It is a figure which shows the frequency distribution (histogram) (a) of a brightness | luminance, the normalized histogram (b), and the block-divided histogram (c). It is a figure ((a) and (b)) explaining deletion of the low-intensity area | region and high-intensity area | region from a brightness | luminance histogram, and a figure ((c) and (d)) explaining the restriction | limiting of a luminance frequency. It is a figure which shows the gradation correction curve showing the image processing conditions (gradation correction conditions) in case a imaging | photography scene is backlight. It is a figure which shows a wavelet function. The waveform of the input signal S _0, is a diagram showing the respective levels of corrected high frequency band components W · gamma waveform obtained by the wavelet transform. It is a system block diagram which shows the filter process of 1 level binomial wavelet transform in a two-dimensional signal. It is a system block diagram which shows the filter process of 1 level binomial wavelet inverse transformation in a two-dimensional signal. It is a block diagram which shows the internal structure of the image adjustment process part 701A. It is a figure which shows the gradation correction curve which set the start point and the end point of the gradation correction curve outside the min value and the max value of the image data value, respectively.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Image processing apparatus 2 Case 3 Magazine loading part 4 Exposure processing part 5 Print preparation part 6 Tray 7 Control part 70 Image processing part 701,701A Image adjustment process part 710,710A Scene discrimination | determination part 712 Ratio calculation part 713 Index calculation part 720 First index calculation unit 721 Second index calculation unit 722 Third index calculation unit 714 Scene discrimination execution unit 715 Color system conversion unit 716 Histogram creation unit 717 Occupancy rate calculation unit 718 First occupancy rate calculation unit 719 Second occupancy rate calculation Units 730 and 730A Gradation correction condition determination units 731 and 731A Gradation correction method determination units 732 and 732A Gradation correction parameter calculation units 733 and 733A Gradation correction amount determination units 740 and 740A Image processing execution units 750 and 750A Noise evaluation units 760 Image reduction unit 702 Film scan data processing unit 703 Reflected original scan data Management unit 704 the image data form decoding processing section 705 image adjustment processing section 706 CRT inherent processing section 707, 708 print-specific processing section 709 image data form creation processing unit 71 data storage unit 8 CRT
9 Film scanner section 10 Reflected document input device 11 Operation section 12 Information input means 13a Card 13b FD
14 Image reading unit 14a PC card adapter 14b FD adapter 15 Image writing unit 15a FD adapter 15b MO adapter 15c Optical disk adapter 16a FD
16b MO
16c Optical disk 30 Image transfer means 31 Image conveying part 32 Communication means (input)
33 Communication means (output)
34 External printer

Claims (18)

A scene discriminating step for discriminating a shooting scene of an image from image data composed of signals of a plurality of pixels;
A gradation correction condition determining step for determining a gradation correction condition based on the result of the scene determination;
Evaluating the amount of noise increase when the gradation correction condition is applied to the image data;
A gradation correction condition adjusting step of adjusting the determined gradation correction condition based on the evaluated noise increase amount;
A gradation correction step of applying a gradation correction process to the image data using the adjusted gradation correction condition;
An image processing method comprising: The scene discrimination step includes
A first occupancy ratio calculating step of dividing the image data into areas each including a combination of predetermined brightness and hue, and calculating a first occupancy ratio indicating a ratio of the entire image data for each of the divided areas. When,
A first index calculating step of calculating the first and second indices by multiplying the first occupancy by different first and second coefficients set in advance, respectively;
The image data is divided into a predetermined area composed of a combination of the distance from the outer edge of the screen and the brightness, and a second occupancy ratio indicating the ratio of the image data to the entire image data is calculated for each of the divided areas. 2 occupancy calculation process;
A second index calculating step of calculating a third index by multiplying the second occupancy by a preset third coefficient;
A third index calculating step of calculating a fourth index by multiplying the average brightness of the skin color at least in the center of the screen of the image data by a preset fourth coefficient;
The image processing method according to claim 1, further comprising: determining a shooting scene of the image data based on the calculated first, second, third, and fourth indices. The gradation correction condition determination step determines a gradation correction method and a gradation correction amount as gradation correction conditions,
The gradation correction condition adjustment step adjusts the determined gradation correction amount based on the noise increase amount,
The gradation correction process performs gradation correction processing on the image data by using the determined gradation correction method and the adjusted gradation correction amount. Image processing method. Obtaining second image data with a reduced image size from first image data composed of signals of a plurality of pixels;
A scene discriminating step for discriminating a photographing scene of the image from the second image data;
A gradation correction condition determining step for determining a gradation correction condition based on the result of the scene determination;
Evaluating a noise increase amount when the gradation correction condition is applied to the first image data;
A gradation correction condition adjustment step of adjusting the gradation correction condition based on the noise increase amount;
A gradation correction step of applying a gradation correction process to the first image data using the adjusted gradation correction condition;
An image processing method comprising: The scene discrimination step includes
Dividing the second image data into regions each having a combination of predetermined brightness and hue, and calculating a first occupancy ratio that indicates a proportion of the entire image data for each of the divided regions. Rate calculation process;
A first index calculating step of calculating the first and second indices by multiplying the first occupancy by different first and second coefficients set in advance, respectively;
The image data is divided into a predetermined area composed of a combination of the distance from the outer edge of the screen and the brightness, and a second occupancy ratio indicating the ratio of the image data to the entire image data is calculated for each of the divided areas. 2 occupancy calculation process;
A second index calculating step of calculating a third index by multiplying the second occupancy by a preset third coefficient;
A third index calculating step of calculating a fourth index by multiplying the average brightness of the skin color at least in the center of the screen of the image data by a preset fourth coefficient;
The method according to claim 4, further comprising: determining a shooting scene of the second image data based on the calculated first, second, third, and fourth indices. Processing method. The gradation correction condition determination step determines a gradation correction method and a gradation correction amount as gradation correction conditions,
The gradation correction condition adjustment step adjusts the gradation correction amount based on the noise increase amount,
The gradation correction step performs gradation correction processing on the first image data using the determined gradation correction method and the adjusted gradation correction amount. 6. The image processing method according to 5. A scene discriminating unit for discriminating a shooting scene of an image from image data composed of signals of a plurality of pixels;
A gradation correction condition determination unit that determines gradation correction conditions based on the result of the scene determination;
A noise evaluation unit that evaluates a noise increase amount when the gradation correction condition is applied to the image data;
A gradation correction condition adjusting unit that adjusts the determined gradation correction condition based on the evaluated noise increase amount;
A gradation correction unit that performs a gradation correction process on the image data using the adjusted gradation correction condition;
An image processing apparatus comprising: The scene discrimination unit
A first occupancy ratio calculation unit that divides the image data into areas each including a combination of predetermined brightness and hue, and calculates a first occupancy ratio indicating a ratio of the entire image data for each of the divided areas. When,
A first index calculation unit for calculating the first and second indices by multiplying the first occupancy by different first and second coefficients set in advance;
The image data is divided into a predetermined area composed of a combination of the distance from the outer edge of the screen and the brightness, and a second occupancy ratio indicating the ratio of the image data to the entire image data is calculated for each of the divided areas. 2 occupancy rate calculator,
A second index calculating unit that calculates a third index by multiplying the second occupancy by a preset third coefficient;
A third index calculating unit that calculates a fourth index by multiplying an average brightness of the skin color at least in the center of the screen of the image data by a preset fourth coefficient;
The image according to claim 7, further comprising: a scene determination execution unit that determines a shooting scene of the image data based on the calculated first, second, third, and fourth indices. Processing equipment. The gradation correction condition determining unit determines a gradation correction method and a gradation correction amount as a gradation correction condition,
The gradation correction condition adjustment unit adjusts the determined gradation correction amount based on the noise increase amount,
9. The gradation correction unit according to claim 7, wherein the gradation correction unit performs a gradation correction process on the image data using the determined gradation correction method and the adjusted gradation correction amount. Image processing apparatus. An image reduction unit for obtaining second image data obtained by reducing the image size from the first image data including signals of a plurality of pixels;
A scene discriminating unit for discriminating a photographing scene of the image from the second image data;
A gradation correction condition determination unit that determines gradation correction conditions based on the result of the scene determination;
A noise evaluation unit that evaluates a noise increase amount when the gradation correction condition is applied to the first image data;
A gradation correction condition adjustment unit that adjusts the gradation correction condition based on the noise increase amount;
A gradation correction unit that performs a gradation correction process on the first image data using the adjusted gradation correction condition;
An image processing apparatus comprising: The scene discrimination unit
Dividing the second image data into regions each having a combination of predetermined brightness and hue, and calculating a first occupancy ratio that indicates a proportion of the entire image data for each of the divided regions. A rate calculator,
A first index calculating step of calculating the first and second indices by multiplying the first occupancy by different first and second coefficients set in advance, respectively;
The image data is divided into a predetermined area composed of a combination of the distance from the outer edge of the screen and the brightness, and a second occupancy ratio indicating the ratio of the image data to the entire image data is calculated for each of the divided areas. 2 occupancy rate calculator,
A second index calculating unit that calculates a third index by multiplying the second occupancy by a preset third coefficient;
A third index calculating unit that calculates a fourth index by multiplying an average brightness of the skin color at least in the center of the screen of the image data by a preset fourth coefficient;
11. A scene discrimination execution unit that discriminates a shooting scene of the second image data based on the calculated first, second, third, and fourth indices. The image processing apparatus described. The gradation correction condition determining unit determines a gradation correction method and a gradation correction amount as a gradation correction condition,
The gradation correction condition adjustment unit adjusts the gradation correction amount based on the noise increase amount,
11. The gradation correction unit performs gradation correction processing on the first image data using the determined gradation correction method and the adjusted gradation correction amount. The image processing apparatus according to 11. On the computer,
A scene discrimination function for discriminating a shooting scene of an image from image data composed of signals of a plurality of pixels;
A gradation correction condition determining step for determining a gradation correction condition based on the result of the scene determination;
A function of evaluating the amount of noise increase when the gradation correction condition is applied to the image data;
A gradation correction condition adjustment function for adjusting the determined gradation correction condition based on the evaluated noise increase amount;
A gradation correction function for performing a gradation correction process on the image data using the adjusted gradation correction condition;
An image processing program for realizing The scene discrimination function is
A first occupancy ratio calculation function that divides the image data into areas each including a combination of predetermined brightness and hue, and calculates a first occupancy ratio indicating a ratio of the entire image data for each of the divided areas. When,
A first index calculation function for calculating the first and second indices by multiplying the first occupancy by different first and second coefficients set in advance;
The image data is divided into a predetermined area composed of a combination of the distance from the outer edge of the screen and the brightness, and a second occupancy ratio indicating the ratio of the image data to the entire image data is calculated for each of the divided areas. 2 Occupancy rate calculation function,
A second index calculation function for calculating a third index by multiplying the second occupancy by a preset third coefficient;
A third index calculation function for calculating a fourth index by multiplying the average brightness of the skin color at least in the center of the screen of the image data by a preset fourth coefficient;
The image processing program according to claim 13, further comprising: a function of determining a shooting scene of the image data based on the calculated first, second, third, and fourth indices. The gradation correction condition determination function determines a gradation correction method and a gradation correction amount as a gradation correction condition,
The gradation correction condition adjustment function adjusts the determined gradation correction amount based on the noise increase amount,
The gradation correction function performs gradation correction processing on the image data using the determined gradation correction method and the adjusted gradation correction amount. Image processing program. On the computer,
A function of acquiring second image data obtained by reducing the image size from the first image data including signals of a plurality of pixels;
A scene discrimination function for discriminating a shooting scene of an image from the second image data;
A gradation correction condition determination function for determining a gradation correction condition based on the result of the scene determination;
A function of evaluating an amount of increase in noise when the gradation correction condition is applied to the first image data;
A gradation correction condition adjustment function for adjusting the gradation correction condition based on the noise increase amount;
A gradation correction function for applying a gradation correction process to the first image data using the adjusted gradation correction condition;
An image processing program for realizing The scene discrimination function is
Dividing the second image data into regions each having a combination of predetermined brightness and hue, and calculating a first occupancy ratio that indicates a proportion of the entire image data for each of the divided regions. Rate calculation function,
A first index calculation function for calculating the first and second indices by multiplying the first occupancy by different first and second coefficients set in advance;
The image data is divided into a predetermined area composed of a combination of the distance from the outer edge of the screen and the brightness, and a second occupancy ratio indicating the ratio of the image data to the entire image data is calculated for each of the divided areas. 2 Occupancy rate calculation function,
A second index calculation function for calculating a third index by multiplying the second occupancy by a preset third coefficient;
A third index calculation function for calculating a fourth index by multiplying the average brightness of the skin color at least in the center of the screen of the image data by a preset fourth coefficient;
17. The image according to claim 16, further comprising: a function of determining a shooting scene of the second image data based on the calculated first, second, third, and fourth indices. Processing program. The gradation correction condition determination function determines a gradation correction method and a gradation correction amount as a gradation correction condition,
The gradation correction condition adjustment function adjusts the gradation correction amount based on the noise increase amount,
The gradation correction function performs gradation correction processing on the first image data using the determined gradation correction method and the adjusted gradation correction amount. The image processing program according to 17.

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