JP2005301337A - Apparatus and method for image processing, and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To properly process a digital image formed under arbitrary conditions on the basis of a composited template image. <P>SOLUTION: An image processing apparatus is equipped with a template information storage part 22 for storing template information as property information on a template image for compositing a digital image, a template selection inputting part 21 for inputting template selection information, a template information reading part 23 for reading the template information out of the template information storage part 22 by using the template selection information, a correction quantity calculating function 30 for calculating a correction quantity from the read template information for an inputted processed image, and an image processing function 40 for processing the processed image by using the calculated correction quantity. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to, for example, an image processing apparatus that processes captured images and the like, and more particularly, to an image processing apparatus that performs correction processing on, for example, a plurality of images captured under different conditions.

  For example, in the printing market such as product flyers, advertisements, use for magazine articles, etc., and in the business market such as exhibitions, seminar materials, site record photographs, real estate and product snapshots, etc., digital cameras (digital still cameras: A wide range of work is performed to arrange a plurality of images such as images (image data, digital images) photographed by DSC) and images read by a scanner in a predetermined area and visualize and output the edited layout image. ing. Conventionally, for example, an image to be laid out is photographed by a professional photographer, and adjusted and edited individually while observing individual image states by a user who is an image processing specialist. On the other hand, in recent years, with the rapid development and popularization of imaging devices represented by digital cameras and mobile phones, and the development of network technologies such as the Internet, images are taken by general users under different and different shooting conditions. There are many opportunities to create a database of multiple images.

  As a prior art disclosing contents for laying out a plurality of images, there is one in which a plurality of images are laid out in a designated area in accordance with an algorithm selected from a plurality of algorithms (see, for example, Patent Document 1). In addition, in order to easily generate the layout of a common leaflet with the same content on which a plurality of products are posted, a rectangular area circumscribing the margin added image is generated for each of the plurality of images and arranged according to a predetermined arrangement rule. A technique for outputting is disclosed (for example, see Patent Document 2).

Japanese Patent Laying-Open No. 2003-101749 (page 4-5, FIG. 4) Japanese Patent Laying-Open No. 2003-101752 (page 3-4, FIG. 1)

  Here, as long as all the images are taken with a constant shooting condition or the like for a plurality of images, it is possible to provide an image that is easy for the user to see even if the images are integrated and output as a layout, for example. . However, a plurality of images taken with different cameras by different photographers under different circumstances and under different photographing conditions are not easy to see even if the layout display is performed as it is. For example, even when limited types of products such as product packages and toys are photographed, various conditions for photographing (photographing location, time, subject position, subject angle, lighting, camera, etc.) are different. However, due to subtle deviations such as the size, position, and inclination of these products, the multiple images displayed in the layout are very unsightly. In addition to these geometric feature amounts, images that are unsightly can also be displayed when the feature values for image quality such as brightness, color, gray balance, and gradation expression are displayed in a disparate layout. Become.

  Furthermore, not only when outputting individually or when outputting a layout, but a type (template) such as a decorative frame and background is determined in advance, and it is required to embed the acquired image in the determined type. There is a case. In such a case, for example, if an image taken under different conditions is embedded as it is, the resulting output image becomes very difficult to see. Also, the output purpose and the like may differ for each template, and if an optimal image can be obtained according to the output purpose, the ease of use for the user can be dramatically improved. Patent Document 1 and Patent Document 2 do not show any processing technique corresponding to such a template.

The present invention has been made to solve the technical problems as described above, and an object of the present invention is to perform an appropriate process based on a template to be synthesized on an image formed under an arbitrary condition. There is to do.
Another object is to obtain a good integrated layout composite image based on template information to be combined when, for example, a plurality of images are combined and output.

  For this purpose, an image processing apparatus to which the present invention is applied receives template information from a template information storage unit that stores template information that is attribute information of a template image for synthesizing a digital image in a predetermined area by a reading unit. The feature amount of the digital image read out and synthesized with the template image is analyzed by the feature amount analyzing means. Based on the feature amount analyzed by the feature amount analyzing unit and the template information read by the reading unit, the image correction amount calculating unit calculates the image correction amount of the digital image.

Here, the correction means for correcting the geometric feature amount and / or the image quality of the digital image using the image quality correction amount calculated by the image correction amount calculation means, and the digital image corrected by the correction means are converted into other digital images. The image processing apparatus may further include output means for outputting an integrated layout together with the image.
Further, the feature amount analyzing means analyzes the geometric feature amount and / or image quality feature amount of the digital image, and the image correction amount calculating means uses the analyzed geometric feature amount and / or image quality feature amount to generate a template. It is possible to calculate a geometric feature amount correction amount and / or image quality correction amount in accordance with information.

  From another viewpoint, the image processing method to which the present invention is applied includes a step of reading a digital image from the image storage means, a step of receiving a selection of a template image to be synthesized with the read digital image, Template information that is attribute information of the template image is acquired from the template information storage means, a geometric feature value and / or image quality feature value of the digital image is analyzed, and analysis is performed using the acquired template information. Correcting the geometric feature quantity and / or image quality feature quantity and pasting the digital image on the template image.

  Here, the step of pasting to the template image can be characterized in that a plurality of digital images are pasted by performing an integrated layout process. This integrated layout process refers to a process of laying out and outputting a plurality of composite images obtained by combining a template image and a processed image on, for example, one sheet of paper or one page screen. Further, the geometric feature value to be corrected can be characterized in that it is the position of the main subject of the digital image with respect to the pasting position of the template image. Further, the image quality feature quantity to be corrected can be characterized in that the brightness of the digital image is corrected based on the brightness of the template image.

  On the other hand, the present invention can be understood as a program to be executed by a computer. This program has a function of reading template information from a template information storage unit that stores template information, which is attribute information of a template image for synthesizing a digital image in a predetermined area, and a digital image to be synthesized with the template image. A function for analyzing the feature value, a function for calculating the image correction amount of the digital image based on the analyzed feature value and the read template information, and correcting the digital image using the calculated image correction amount And a function of synthesizing the corrected digital image with a template image read from a predetermined storage means.

  According to the present invention, for example, an image formed under an arbitrary condition can be subjected to appropriate processing based on a template to be synthesized.

Embodiments of the present invention will be described below in detail with reference to the accompanying drawings.
FIG. 1 is a diagram illustrating an overall configuration example of an image processing system to which the exemplary embodiment is applied. Here, each function is connected via a network 9 such as the Internet. An image processing system shown in FIG. 1 includes an image processing server 1 that performs integrated layout processing of images that are distributedly shot, an image database server 2 that acquires images that are distributedly shot and selects images to be subjected to integrated layout processing, and an image database One or a plurality of image databases (image DBs) 3 that are connected to the server 2 and store images taken in a distributed manner are provided. In addition, the image captured by the digital camera 4 serving as a photographing unit is read, and the image transferred by the image database server 2 via the network 9 and the image corrected by the image processing server 1 are displayed. A display device 6 for output and a printing image processing device 8 for performing various image processing for outputting an image corrected by the image processing server 1 to a printer 7 as an image print output means are provided.

  The image transfer device 5, the display device 6, and the printing image processing device 8 can be configured by a computer device such as a notebook computer device (notebook PC) or a desktop computer. Further, the image processing server 1 and the image database server 2 can be grasped as various computer devices such as a PC. The present embodiment is characterized in that a plurality of images that are dispersedly photographed at different photographing locations and different photographing conditions are combined with a predetermined template. Therefore, a plurality of digital cameras 4 and a plurality of image transfer apparatuses 5 connected thereto are connected to a network 9 and arranged at various places. Also, the digital camera 4 may be connected to the network 9 via a relay station (not shown), for example, using its own communication function, and directly transmit the captured image to the image processing server 1. . In addition, if the wireless LAN function is provided, for example, a mode in which the photographed image is directly transmitted from the digital camera 4 to the image processing server 1 by connecting to the network 9 via an access point (not shown) is conceivable.

FIG. 2 is a block diagram showing functions of the image processing server 1 for executing image processing in the present embodiment. The image processing server 1 obtains image data (digital image) stored in the image DB 3 from the image database server 2, and an image number (Gn) for a plurality of images input by the image input unit 11. A numbering / total number counting processing unit 12 that executes preprocessing such as image numbering and total number counting, and an image output unit 13 that sends individual images subjected to image processing individually or in a layout arrangement to the network 9. I have. Further, for example, a template selection input unit 21 for inputting a template selection from a user terminal (image transfer device 5, display device 6, printing image processing device 8, digital camera 4, etc.) via the network 9, for example, a hard disk drive (HDD) ) And the like, and the template information storage unit 22 in which various template images and template information (template attribute information) are stored, and the template information storage unit 22 using the selection information input by the template selection input unit 21. A template information reading unit 23 for reading predetermined template information is provided. Here, the template stored in the template information storage unit 22 includes, for example, a frame in which a digital image such as a photographic image is fitted, a background image, a type in which a digital image is incorporated, and the like. In the present embodiment, the template image means the image itself to be combined, and the template information means attribute information that the template image has.
Note that template selection information may be included or attached to individual image data. In such a case, the template selection input unit 21 acquires template selection information from the image input unit 11 when individual image data is input by the image input unit 11.

  In addition, the image processing server 1 determines the feature amount of each individual image input from the image input unit 11 and subjected to preprocessing such as image number (Gn) assignment and total number count by the number assignment / total number count processing unit 12. A correction amount calculation function 30 that analyzes and calculates an image correction amount, executes various image processing based on the correction amount of the individual image calculated by the correction amount calculation function 30, and performs a composition process using a template image An image processing function 40 is provided. In the correction amount calculation function 30, the feature amount is extracted by the image feature amount extraction unit 31 and the image feature amount extraction unit 31 that extract the feature amount such as the geometric feature amount and the image quality feature amount in the image to be corrected. An image feature amount analyzing unit 32 that analyzes the image feature amount based on the feature amount of the image and the template information of the template image to be synthesized, and an image that calculates the correction amount of the image based on the analysis result by the image feature amount analyzing unit 32 A correction amount calculation unit 33 is provided. The geometric feature amount is a feature amount such as a position, a center of gravity position, a size (vertical × horizontal size), an image direction (vertical, horizontal, or diagonal) of a main subject (main object) included in the digital image (processed image). The image quality feature amount is a feature amount related to image quality such as brightness, color, gray balance, and gradation correction. The image quality feature amount is grasped separately from the main subject and the background. Further, the image processing function 40 includes a geometric feature amount correction unit 41 that corrects a geometric feature amount such as the position, size, and inclination of the object recognized as the main subject, brightness, color, gray balance, and tone correction. An image quality correction unit 42 that corrects image quality, a background processing unit 43 that corrects a background such as background removal and background unification, and a synthesis processing unit 44 that performs synthesis with a template image and integrated layout processing. .

  The image quality correction unit 42 includes, for example, a smoothing process for performing noise suppression processing, brightness correction for moving a reference point depending on whether the image distribution is closer to the brighter side or the darker side, a brighter part of the image distribution, Functions include highlight shadow correction for adjusting the distribution characteristics of the shadow portion and light / dark contrast correction for correcting the contrast of light and dark by obtaining the distribution state from the light / dark distribution histogram. In addition, for example, a hue / color balance correction that corrects a color shift of a white portion on the basis of a white area portion considered to be the brightest, for example, a gray color so that it is bright for an image having a slightly low saturation. Saturation correction that performs processing such as reducing the saturation and making the eye close to the image, for example, correcting the skin color closer to a predetermined value with the skin color as a reference, memory color correction that corrects a specific memory color It has each function. Furthermore, it is possible to provide a sharpness enhancement function that determines the strength of the edge from the overall edge degree and corrects the image to a crisp image (sharp image), for example.

Next, the flow of processing executed in the block diagram shown in FIG. 2 will be described.
FIG. 3 is a flowchart showing processing executed by the image processing server 1. First, the processed image is input by the image input unit 11 of the image processing server 1, and the total number N of processed images is counted by the number assignment / total number processing unit 12 (step 101). The number assigning / total count processing unit 12 assigns the image number Gn of the processed image (step 102). Next, the geometric feature amount is extracted from the processed image by the image feature amount extraction unit 31 of the correction amount calculation function 30 (step 103), and the image quality feature amount is extracted from the processed image (step 104). The correction amount calculation function 30 reads the template information of the template image to be combined from the template information reading unit 23 (step 105), and the image feature amount analysis unit 32 reads out the feature amounts of the extracted processed image. The geometric feature amount and the image quality feature amount are analyzed using the template information (step 106). Further, the image correction amount calculation unit 33 calculates an image correction amount using the analyzed result (step 107), and provides it to the image processing function 40.

  The image processing function 40 performs various image processing on the processed image using the image correction amount provided from the correction amount calculation function 30 (step 108). Then, the processed image is temporarily stored in a memory such as a RAM. Thereafter, it is determined whether or not the correction has been completed for the number of images required for the integrated layout process (step 109). For example, when a predetermined number of images (for example, four, eight, twelve, etc.) are laid out in one output image, it is determined whether or not the predetermined number of processes have been completed. If correction of the integration number has not ended, the process returns to step 102 and is repeated. When the correction of the integration number is completed, the template image to be synthesized is read from the template information reading unit 23, and the synthesis processing unit 44 pastes the template image on the image that has been subjected to the image processing. Are synthesized (step 110). Further, an integrated layout process is performed in the synthesis processing unit 44 (step 111), and the processed image is output from the image output unit 13 (step 112). Here, it is determined whether or not the image number Gn has reached the total number N, that is, Gn <N (step 113). If Gn <N, the process returns to step 102 and the process is repeated. If Gn <N, the process ends. The simplest layout method is to arrange the processed images in the order in which they are read. However, the layout can be arbitrarily set by the user. The setting for such a user can be obtained from each user terminal via the network 9.

Next, the main processing of the processing flow shown in FIG. 3 will be described in further detail.
FIGS. 4A to 4D are diagrams for explaining the geometric feature amount extraction process of the processed image shown in step 103 of FIG. 3. FIG. 4A shows an example of a processed image input to the image feature amount extraction unit 31. Here, a subject is composed of a plurality (four) of objects. The image feature quantity extraction unit 31 first executes a binarization process as shown in FIG. 4B when extracting a geometric feature quantity. Next, a labeling process as shown in FIG. 4C is performed on the binarized image. Here, labeling is performed on the three image elements L1 to L3. Thereafter, as shown in FIG. 4D, the maximum circumscribed rectangle including all the labeled image elements is calculated. As the maximum circumscribed rectangle calculated, for example, if the coordinate axis is taken from the upper left, the top minimum (Top min), the left minimum (Left min), the bottom maximum (Bottom max), the right maximum (Right max) Yes, each of these is calculated. As described above, the position coordinates of the main subject (main object) can be obtained. However, as geometric features, characteristics other than the above, for example, the image size, the image direction (vertical / horizontal), etc., should also be grasped. Can do.

FIG. 5 is a flowchart for explaining the image quality feature amount extraction step shown in step 104 of FIG. The image feature quantity extraction unit 31 first reads a processed image (step 201), and separates the object and the background (step 202). Since there are many conventional methods for separating the object and the background, description thereof is omitted here. Thereafter, the following processing is executed for each of the separated object and background. That is, luminance extraction is performed from step 203 to step 205, distribution distribution of R (red), G (green), and B (blue) is extracted from step 206 to step 210, and saturation is extracted from step 211 to step 213. In the luminance extraction, first, a luminance color difference type color signal is converted into, for example, L ^* a ^* b ^* , luminance conversion is performed (step 203), and a luminance histogram is collected (step 204). Thereafter, the distribution average value L_ave is calculated (step 205). This luminance conversion is used, for example, for highlight shadow correction and light / dark contrast correction. For example, in light / dark contrast correction, a light / dark distribution (for example, a histogram) is taken from a reference image, and the degree of distribution is grasped, for example, with a range of about five steps. The degree of this distribution is grasped for each of the background and the main subject (subject) separated from the background.

  On the other hand, in RGB distribution extraction, RGB conversion is first performed (step 206), and an RGB histogram is collected (step 207). Then, r_max that is the maximum value (Max value) of the R distribution (step 208), g_max that is the maximum value of the G distribution (step 209), and b_max that is the maximum value of the B distribution (step 210). Is made. For example, when performing hue / color balance correction, RGB are separately collected in the histogram as described above.

Further, in order to perform saturation correction, saturation conversion is performed (step 211). First, with respect to the main subject separated from the background, a saturation histogram is collected (step 212), and a distribution average value S_ave is calculated (step 213). Here, the saturation can be expressed by two planes a ^* b ^* of L ^* a ^* b ^* . When a ^* b ^* is 00, it becomes gray. As processing, in addition to correction to match the template image, for example, the one closer to gray is shrunk to gray, that is, if it is slightly colored, the saturation is reduced and the correction is made in the direction of becoming gray. It can also be configured to correct. It is also effective to perform saturation correction so as to enhance vividness in a distribution where the saturation is medium to high.
In addition to the above, the extracted image quality feature amount includes, for example, image resolution.

Next, the reading of template information shown in step 105 of FIG. 3 and the analysis of the geometric feature value and image quality feature value shown in step 106 will be described.
FIG. 6 is a diagram for explaining a template image and template information (template attribute information) stored in the template information storage unit 22. The template information storage unit 22 stores a template collection, which is a set of template images (templates) to be combined, and template attribute information for determining the correction amount of the processed image at the time of combination in a mutually compatible state. Has been. Examples of template information (template attribute information) include, as image frame information, the vertical / horizontal size of the composite part, the vertical / horizontal image direction, the image resolution, the composite position coordinates of the main object to be composited, the background color (ambient color ) Etc. are stored. In the vertical × horizontal size of the composite portion, for example, a rectangular range is set as the maximum size of the image to be pasted. Even when the frame of the synthesis location has a curve such as a circle or an ellipse, for example, the maximum size that can be synthesized without missing an image is set. For example, in the case of a circle, it is a rectangular range whose diameter is a diagonal line, and in the case of an ellipse, it is a rectangular range in which four corners are inscribed. In addition, the image resolution may vary depending on the manner in which each template is used. For example, about 100 dpi (dots / inch) is preferable if it is only displayed on a display as a Web page, and about 200 dpi or 400 dpi is preferable for printing. The resolution information is very meaningful. Furthermore, the synthesis position coordinates of the main object are meaningful information for synthesizing the position of the main subject (main object) of the processed image into a position where it can be easily seen.

In addition, as text frame information, a font of a text to be synthesized, a size, a distinction between vertical writing and horizontal writing, a paragraph style, and the like are stored. The template information (template attribute information) is provided to the template information reading unit 23 together with the designated template image based on the template image designation by the template information reading unit 23.
After that, as shown in step 106 of FIG. 3, the image feature amount analysis unit 32 uses the feature amounts of the processed image extracted as described above and the read template information, and the geometric feature amount and image quality. The feature amount is analyzed. Further, as shown in step 107, the image correction amount calculation unit 33 calculates the image correction amount using the result analyzed by the image feature amount analysis unit 32. Thereafter, correction of the processed image is performed.

  FIGS. 7A to 7C are diagrams showing an example in which correction based on a template image is performed on four images, and a combined image is output by performing integrated layout processing, in the case of the integration number 4. It is. FIG. 7A shows a predetermined template selected and read from the template collection in the template information storage unit 22. FIG. 7B shows four processed images read from the image database server 2 and input to the image input unit 11. Further, in FIG. 7C, after the four processed images shown in FIG. 7B are corrected, the template image shown in FIG. The results made are shown. In the example shown in FIG. 7B, the position of the main subject (main object) of each of the four processed images is shifted from the center. In addition, the main subject is not appropriately sized relative to the size of the template attachment position. Therefore, by analyzing the geometric feature amount described above, the vertical and horizontal sizes of the processed image are grasped, the vertical and horizontal sizes are changed using the template information, and the recognized main subject is pasted to the template image. It adjusts so that it may become the center position of a position, and is compounded. In addition, since the size of the template image to be displayed (output) differs depending on the number of integrations, an operation of changing the vertical x horizontal size of the processed image according to the number of integrations is also performed.

  In the correction relating to the image quality, for example, processing such as changing the background color in accordance with the color of the template image is performed. For example, if the template is a dark color, the background is set to a bright color, and if the template is a light color, the background is set to a dark color. Conversely, it is also effective to set the brightness to the same series depending on the user's preference. This user preference can be acquired from the user terminal via the network 9. Also, the brightness of the main subject can be changed according to the set background and the brightness of the selected template. Similarly, it is also effective to change the color of the background and main subject according to the color of the selected template.

  Further, the processed image (digital image) stored in the image database 3 can be linked to a product database (not shown). In this product database, product name, model number, manufacturer name, product type, and other various information can be stored in correspondence with the identification number (image No.) of the processed image. When the processed image is fitted into the template image, the product information can be read from the product database in correspondence with the processed image and can be written in the text pasting column of the template image. At this time, it is also effective to correct the text information to be written based on the template information. For example, an optimal text color is selected according to the color of the frame of the template image.

  As described above in detail, according to the present embodiment, it is possible to automatically correct, for example, an image captured and acquired in accordance with a template to be pasted, and to obtain a good-looking image. Can do. At this time, it is possible to easily acquire a product layout that is very easy for the user to view by automatically correcting, for example, subtle deviations such as the size, position, and tilt of a plurality of products obtained by photographing. it can. In addition, the layout image can be easily changed simply by changing the design of the template image. Thereby, for example, renewal of a Web page can be easily performed.

  Note that this embodiment can be assumed to be used in various types such as an application type, a printer driver type, and a cooperation type with a digital camera. In the application type, for example, it can be used for a function of automatically adjusting a collected image of a user as a plug-in of software for albuming or managing a digital still camera (DSC) image. In the printer driver type, the driver setting can be selected as an optional function, or can be a function incorporated in the mode setting itself. Further, in the cooperation type with the digital camera, the present embodiment can be applied as a function for embedding tag information in a file format and enabling an adjustment instruction at the printing stage.

  In addition, when the computer program to which the present embodiment is applied is provided to the computer, the computer can read the program to be executed by the computer, in addition to the case where the computer program is installed in the computer, for example. A form provided by a storage medium stored in the above is conceivable. Examples of the storage medium include various DVDs, CD-ROM media, card-type storage media, and the like, and a program is read by a DVD, CD-ROM reader, card reader, or the like provided in each of the above computers. Then, this program is stored in various memories such as an HDD and a flash ROM provided in each computer, and is executed by the CPU. These programs may be provided to a computer via a network from a program transmission device, for example.

  Examples of utilization of the present invention include, for example, a computer connected to an image forming apparatus such as a printer, a server that provides information via the Internet, a digital camera, a program executed by these various computer devices, and the like. There is utilization of.

It is the figure which showed the example of whole structure of the image processing system to which this Embodiment is applied. It is a block diagram which shows the function of the image processing server for performing the image processing in this Embodiment. It is a flowchart which shows the process performed with an image processing server. (a)-(d) is a figure for demonstrating the extraction process of the geometric feature-value which a process image has. It is a flowchart for demonstrating the extraction process of an image quality feature-value. It is a figure for demonstrating the template image and template information (template attribute information) stored in the template information storage part. (a)-(c) is the figure which showed the example based on which the correction based on a template image is performed about four images, an integrated layout process is performed, and a synthesized image is output.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 ... Image processing server, 2 ... Image database server, 3 ... Image database (image DB), 4 ... Digital camera, 5 ... Image transfer apparatus, 6 ... Display apparatus, 7 ... Printer, 8 ... Image processing apparatus for printing, 9 DESCRIPTION OF SYMBOLS ... Network, 11 ... Image input part, 12 ... Number assignment and total number processing part, 13 ... Image output part, 21 ... Template selection input part, 22 ... Template information storage part, 23 ... Template information reading part, 30 ... Correction amount Calculation function 31 ... Image feature value extraction unit 32 ... Image feature value analysis unit 33 ... Image correction amount calculation unit 40 ... Image processing function 41 ... Geometric feature value correction unit 42 ... Image quality correction unit 43 ... Background Processing unit, 44... Synthesis processing unit

Claims (10)

A reading unit that reads the template information from a template information storage unit that stores template information that is attribute information of a template image that synthesizes a digital image in a predetermined region;
A feature amount analyzing means for analyzing a feature amount of the digital image combined with the template image;
An image processing apparatus comprising: an image correction amount calculating unit that calculates an image correction amount of the digital image based on the feature amount analyzed by the feature amount analyzing unit and the template information read by the reading unit. .   The image processing apparatus according to claim 1, further comprising a correction unit that corrects a geometric feature amount and / or image quality of the digital image using the image quality correction amount calculated by the image correction amount calculation unit.   The image processing apparatus according to claim 2, further comprising an output unit that outputs the digital image corrected by the correction unit in an integrated layout together with another digital image. The feature amount analyzing means analyzes a geometric feature amount and / or image quality feature amount of the digital image,
The image correction amount calculating means calculates the geometric feature amount correction amount and / or the image quality correction amount according to the template information using the analyzed geometric feature amount and / or the image quality feature amount. The image processing apparatus according to claim 1, wherein: Reading a digital image from the image storage means;
Receiving a selection of a template image on which the read digital image is synthesized;
Obtaining template information that is attribute information of the selected template image from a template information storage unit;
Analyzing the geometric feature and / or image quality feature of the digital image;
Correcting the analyzed geometric feature value and / or the image quality feature value using the obtained template information, and pasting the digital image on the template image.   6. The image processing method according to claim 5, wherein the step of pasting to the template image includes pasting the plurality of digital images by performing an integrated layout process.   6. The image processing method according to claim 5, wherein the geometric feature value to be corrected is a position of a main subject of the digital image with respect to a pasting position of the template image.   6. The image processing method according to claim 5, wherein the image quality feature value to be corrected is the brightness of the digital image corrected based on the brightness of the template image. On the computer,
A function of reading the template information from the template information storage means for storing the template information that is the attribute information of the template image for synthesizing the digital image in a predetermined area;
A function of analyzing a feature amount of the digital image combined with the template image;
A program for realizing a function for calculating an image correction amount of the digital image based on the analyzed feature amount and the read template information. A function of correcting the digital image using the calculated image correction amount;
The program according to claim 9, further causing the computer to realize a function of synthesizing the corrected digital image with the template image read from a predetermined storage unit.

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