JP2009060431A - Image processor, image processing method, and image processing program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image processor or the like capable of correcting images with a simple operation. <P>SOLUTION: The tint of an image (original image) read from a memory slot 3 is converted with an image read by a scanner 2 as a sample. In this case, the image processor 1 determines a hue region to be subjected to image correction from an image read by the scanner 2, and allows a user to select a desired amount of feature. With a region corresponding to the selected amount of feature as a sample image, an original image is corrected. By reading a sample image by the scanner 2, desired image correction can be performed to an image read from the memory slot 3 with a simple operation for simply selecting a desired tint. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a printing apparatus capable of correcting an image with an easy operation.

  With the popularization of digital cameras and the like, it has become popular to print images taken by individuals using a home printing device. In addition, printing (copying) an image stored on a print medium with a home printing apparatus is also widespread. In such a case, if the acquired image does not have a desired color or tone, it is desired to correct the image before printing.

  Here, for example, Patent Document 1 below discloses a printing apparatus capable of image adjustment. In this printing apparatus, it is necessary to input adjustment data in order to adjust the image quality of the acquired image.

JP 2007-89179 A

  In the printing apparatus as described above, it is necessary to input specific data in order to adjust the image, so knowledge about the image is required, and sensory color adjustment cannot be performed. Further, the above document does not disclose anything about image adjustment of only a specific region. Further, in the above document, since it is necessary to select or set adjustment data, it is not a simple operation. Further, when determining a color to be subjected to image correction processing, for example, it is highly accurate to specify whether the target of image correction processing (a user's target position) is a dark color portion or a light color portion. This is desirable from the viewpoint of image correction processing.

  In order to solve the above problems, an object of the present invention is to provide an image processing apparatus capable of performing desired image correction with high accuracy by an easy operation.

In order to achieve the above object, an invention according to claim 1 is an image processing apparatus including a first image input unit, a second image input unit, a hue specifying unit, a designated area determining unit, a feature amount extracting unit, and an image correcting unit. The first image input means inputs a first image, the second image input means inputs a second image, and the hue specifying means is the most of the hue components constituting the first image. A hue component having a large component ratio is identified as a specific hue, and the designated area determining unit divides the first image into a plurality of areas, extracts the specific hue area for each of the plurality of areas, For each region, a display feature amount is extracted from the extracted region, information about a plurality of display feature amounts corresponding to each of the plurality of regions is displayed on a display unit, and the display displayed on the display unit One feature The display feature amount is selected by the user, a divided region corresponding to the one display feature amount is determined as the designated region, and the feature amount extraction unit is configured to select the first feature based on the designated region in the first image. A second feature amount is extracted based on the second image, and the image correction unit corrects the second image based on the first feature amount and the second feature amount. And
The first and second image input means may be composed of the same input means.

  According to a second aspect of the present invention, in the image processing apparatus according to the first aspect, among the areas divided by the designated area determining means, the ratio of the specific hue area in the divided area is a predetermined area or less. The corresponding display feature amount is excluded from the display target.

  The invention according to claim 3 is the image processing apparatus according to claim 1 or 2, further comprising correction data generation means for generating correction data based on the first feature quantity and the second feature quantity, and the image correction means. Is characterized in that the second image is corrected based on the correction data.

  According to a fourth aspect of the present invention, in the image processing apparatus according to the first or second aspect, the feature amount extraction unit extracts a feature amount related to a hue, and the first image and the second image are extracted based on the feature amount related to the hue. Each of the images is divided into a plurality of regions, and a feature amount related to a color other than the hue is extracted for each of the divided regions, and the correction data creating unit is configured to perform the above processing for each divided region of the first image and the second image. A representative value of a feature value related to hue is extracted, hue correction data is created based on the representative value, and a representative value of feature value related to other than the hue is determined for each divided area of the first image and the second image. A value is extracted, and the image correction unit corrects a feature amount related to a hue for each pixel in the second image based on the hue correction data, and relates to a feature other than a hue for each pixel of the second image. The feature amount, and correcting on the basis of the representative value of each of said areas.

  According to a fifth aspect of the present invention, in the image processing apparatus according to any one of the first to fourth aspects, the hue specifying unit specifies a hue based on an H value in the HSV space.

  According to a sixth aspect of the present invention, in the image processing device according to any one of the first to fifth aspects, the display feature amount is a feature amount related to brightness and / or saturation.

  According to a seventh aspect of the present invention, in the image processing apparatus according to any one of the first to sixth aspects, the first image input means is configured to read an image optically read from a print medium set at a predetermined reading position. The second image input means inputs image information stored in the print medium as a second image.

  The invention according to claim 8 is the image processing apparatus according to any one of claims 1 to 7, further comprising print control means, wherein the print control means performs a process for printing the corrected second image information. It is characterized by performing.

  The invention according to claim 9 is the image processing method, wherein the first image input step of inputting the first image, the second image input step of inputting the second image, and the hue component constituting the first image are the most. A hue specifying step for specifying a hue component having a large component ratio as a specific hue; a dividing step for dividing the first image into a plurality of regions; a specific hue extracting step for extracting the region of the specific hue for each of the plurality of regions; A display amount feature amount extracting step for extracting a display feature amount from the extracted region for each of the plurality of regions; a display step for displaying information on a plurality of display feature amounts corresponding to each of the plurality of regions on a display unit; A feature amount selection step for allowing a user to select one display feature amount from the display feature amounts displayed on the display unit; an amount corresponding to the one display feature amount A designated area determining step for determining an area as a designated area; a first feature quantity extracting step for extracting a first feature quantity based on the designated area in the first image; and a second feature quantity based on the second image. The method includes a second feature amount extraction step to be extracted, and an image correction step of correcting the second image based on the first feature amount and the second feature amount.

  According to a tenth aspect of the present invention, in the image processing program, a first image input step for inputting a first image, a second image input step for inputting a second image, and a hue component constituting the first image in a computer. Among them, a hue specifying step for specifying a hue component having the largest component ratio as a specific hue, a dividing step for dividing the first image into a plurality of regions, and a specific hue for extracting the region of the specific hue for each of the plurality of regions An extraction step, a display amount feature amount extraction step for extracting a display feature amount from the extracted region for each of the plurality of regions, and information on a plurality of display feature amounts corresponding to each of the plurality of regions is displayed on the display unit A display step for performing, a feature amount selecting step for allowing a user to select one display feature amount from the display feature amounts displayed on the display unit, A designated area determining step for determining a divided area corresponding to the display feature quantity as a designated area; a first feature quantity extracting step for extracting a first feature quantity based on the designated area in the first image; A second feature amount extracting step for extracting a second feature amount based on the image; and an image correcting step for correcting the image of the second image based on the first feature amount and the second feature amount. And

  According to the first aspect of the present invention, a color area to be used as a model in an image read by a scanner or the like is determined, and a predetermined parameter is indirectly selected by the user, thereby performing highly accurate image correction processing. It can be carried out. Various parameters can be used to specify the hue.

  According to the second aspect of the present invention, since the divided area in which the determined color area occupies the divided area is smaller than the predetermined amount, the divided area is not substantially selected because it is not displayed. Therefore, the divided area can be excluded from the candidate model areas. Thereby, it is possible to prevent correction that is not intended by the user.

  According to the invention which concerns on Claim 3, there can exist the effect mentioned above.

  According to the fourth aspect of the invention, since the image is divided into a plurality of regions based on the hue, the region can be divided based on human perception, and color correction with high accuracy can be performed. Further, since different image correction processes are performed separately for the feature quantity related to hue and the feature quantity related to other than the hue, a desired image correction process can be easily performed.

  According to the invention which concerns on Claim 5, a hue can be specified based on H value in HSV space.

  According to the invention which concerns on Claim 6, the feature-value regarding brightness and / or saturation can be made into the feature-value for a display.

  According to the seventh aspect of the present invention, a model image can be input from a scanner or the like, and an image read from a storage medium can be a target for image correction.

  According to the eighth aspect of the invention, since the print control unit is provided, an image whose image has been corrected can be printed.

  The inventions according to claims 9 and 10 can also achieve the effects described above.

[First embodiment of the present invention]
First, an example of the appearance of the image processing apparatus according to the present invention is shown in FIG. An example of the internal configuration of the image processing apparatus 1 is shown in FIG.

  The image processing apparatus 1 is an example of a multi-function printer or the like, and has a scanner 2 for reading image information from a photograph or the like on the upper part (in FIG. 1, the scanner is in a covered state). .) Image information read by the scanner is used as sample information for correcting the color and the like of image information read from the memory slot shown below. Details of the image correction process will be described later.

  The image processing apparatus 1 has a memory slot 3 that functions as an IC card reader for reading image information recorded in an external memory that is a recording medium such as an SD card (trademark) or a CF card (trademark). Here, as the external memory (storage medium), a known medium and format can be appropriately selected. Further, it may be configured to have a plurality of memory slots so as to be applied to a plurality of types of media. In addition, a communication function (for example, a LAN card, not shown) that can be connected to a network such as a LAN to which a PC storing image information is connected is added to the signal line to which the CPU, ROM, RAM, etc. shown in FIG. 2 are connected. Thus, instead of the external memory, desired image information may be read from a network to which the PC is connected. In that case, the image processing apparatus 1 has a communication function.

  The image processing apparatus 1 has an operation panel 4 for performing various operations. Various buttons and the like are arranged on the operation panel 4.

  The image processing apparatus 1 includes a printer 10 illustrated in FIG. The image information subjected to the image correction is printed by the printer 10. The printed paper is discharged from the discharge port 5, for example.

  The image processing apparatus 1 has display means 6. Various character information and images can be displayed. In addition, a touch panel may be provided on the front surface of the display means 6 and a part or all of the functions of the operation panel 4 may be substituted. Moreover, you may comprise so that a touch panel and the operation panel 4 can be used together.

  The image processing apparatus 1 is entirely controlled by a processor (CPU 9). In addition, there are a RAM 7 for temporarily storing information and a ROM 8 for storing predetermined information. Moreover, you may have mass storage means, such as HDD, as needed.

  Each element mentioned above is mutually connected by the signal wire | line.

  Further, various functions (for example, FAX) can be added to the image processing apparatus 1. Further, an interface for connecting to a personal computer or the like and an interface for connecting to another printing apparatus may be provided.

  In addition, the said external appearance is only an example, and it cannot be overemphasized that this invention is not limited to the said external appearance. Moreover, in the above-mentioned external appearance, an apparatus in which all functions are integrated is shown, but some functions may be realized by an external apparatus. Furthermore, the internal configuration is merely an example, and it goes without saying that the present invention is not limited to the internal configuration.

  Next, an outline of user operations and processing of the apparatus main body in basic processing using the image processing apparatus 1 according to the present invention will be described. FIG. 3 shows an outline of the processing. First, the user sets a memory in which an image to be corrected is stored in a memory slot of the image processing apparatus. As described above, as this memory, a known storage medium such as a memory card can be appropriately employed.

  When the memory is set in the memory slot 3 (1), the image processing apparatus recognizes the set memory and causes the user to select an image to be corrected from the images stored in the memory. (2). In addition, the process for selecting an image can employ | adopt suitably a well-known technique (For example, switching and displaying an image sequentially, etc.). Further, the display means 6 may be used.

  When an image to be corrected is determined (3), the image processing apparatus reads the image and writes it in, for example, a RAM (4). Hereinafter, the image may be referred to as an “original image”.

  Next, the user places (5) on the scanner an image including an area that serves as a sample for image correction of the image (hereinafter, referred to as “whole image”), and presses a predetermined button or the like. The image processing apparatus writes the entire image in, for example, the RAM 7 (6).

  By processing the entire image, a hue (hereinafter, also referred to as “specific hue”) as a sample for correcting the original image is determined (7). The processing content for the entire image will be described later.

  The entire image is divided into a plurality of regions, a predetermined feature amount is extracted for each divided region, and information on the extracted feature amount is presented to the user (8). Specific processing will be described later.

  The user selects desired information from the presented information (9). Specific processing will be described later.

  The original image is corrected using the feature amount corresponding to the selected information (10). Specific processing will be described later.

  In the process of FIG. 3 described above, an image is read using a scanner after reading an image from the memory. However, the entire image is read using the scanner first, and then the image is read from the memory. It may be configured. Further, an original image that has undergone image correction may be printed.

  In addition, an original image that has undergone image correction may be printed.

[First Embodiment of Basic Processing of the Present Invention]
Next, basic processing in the image processing apparatus 1 will be described. FIG. 4 is a flowchart showing an outline of the first embodiment of the basic processing of the present invention.

  In S <b> 11, the original image and the entire image are read into the RAM 7 of the image processing apparatus 1. Here, the format of the read image is not limited.

  In S12, each pixel of the read entire image is converted into an HSV parameter. The conversion process to the HSV parameter will be described later.

  In S13, a specific hue is determined from the entire image. Specific processing for determining the specific hue will be described later.

  In S14, each pixel of the read original image is converted into an HSV parameter. In S15, the second feature value is extracted as the feature value of the original image based on the HSV parameter.

  In S16, the original image is corrected based on the first feature value and the second feature value. Specific processing for color correction will be described later.

  In S17, the corrected original image is printed. At this time, the HSV parameters are converted into other parameters (for example, RGB parameters) as necessary. The conversion process to RGB parameters will be described later. Note that a configuration may be adopted in which printing processing is not performed as necessary.

  In the above-described processing, L * c * h * parameters may be extracted instead of HSV parameters. Further, the present invention can be realized even with known parameters such as RGB parameters. In the following, a case where HSV parameters are extracted will be described.

  Next, an example in which the pixels of the entire image in S12 of FIG. 4 are converted into HSV parameters will be described. In the following, it is assumed that the entire image is in RGB format. Conversion from the RGB format to the HSV format can be performed as follows. In addition, the conversion formula shown below is an example, and it cannot be overemphasized that you may convert with another conversion formula.

[RGB → HSV conversion formula]
max (a, b, c) represents the largest value among a, b, and c.
min (a, b, c) represents the smallest value among a, b, and c.
V = max (R ÷ 255, G ÷ 255, B ÷ 255)
When V is not 0
S = {V-min (R, G, B)} ÷ V
When V is 0
S = 0
When {V-min (R, G, B)} is not 0,
r = (V-R ÷ 255) ÷ (V-min (R, G, B)
g = (V-G ÷ 255) ÷ (V-min (R, G, B)
b = (V-B ÷ 255) ÷ (V-min (R, G, B)
When {V-min (R, G, B)} is 0,
r = 0
g = 0
b = 0
When V = R ÷ 255
H = 60 × (bg)
When V = G ÷ 255
H = 60 × (2 + rg)
When V = B ÷ 255
H = 60 × (4 + gr)
However, when H <0
H = H + 360

  Next, the process of determining the specific hue in S13 described above will be described. FIG. 5 is a flowchart of the hue specifying process. In the following processing, the H value takes a value of −30 to less than 330. When the H value is not within the above range, by appropriately converting the H value (for example, “H value + 360 × n” or “H value−360 × n”, n is an integer), the value in the above range is taken. adjust.

First, in S21, the entire image is divided into a plurality of regions. Here, the image is divided based on six commonly used hues. Specifically, based on the H value of each pixel,
-R region: -30 or more and less than 30-Y region: 30 or more and less than 90-G region: 90 or more and less than 150-C region: 150 or more and less than 210-B region: 210 or more and less than 270-M region: Divide into 270 or more and less than 330. That is, a process of classifying the constituent pixels of the model image into six classification items according to the classification standard corresponding to the hue value is performed. Here, the correspondence relationship between the region and the H value is an example and can be changed as appropriate.

In S22, the ratio of the divided area in the entire image is calculated. Here, the ratio of the entire image is defined as follows.
-Ratio of the R region in the entire image: sRateR
-Ratio of the G area in the entire image: sRateG
-Ratio of B area in the whole image: sRateB
-Ratio of the C area in the entire image: sRateC
-Ratio of the M area in the entire image: sRateM
The ratio of the Y area in the entire image: sRateY

Here, the above ratio is, for example, for the B region.
Although it can be defined as sRateB = (number of pixels in the B region in the entire image) / (total number of pixels in the entire image), it may be defined by another formula.

  Next, in S23, the largest area among the divided areas (hereinafter, sometimes referred to as “example candidate area”) is specified, and the hue of the area is determined as the specific hue.

  In S24, it is determined which part of the model candidate area is to be the model feature. For example, when the image shown in FIG. 6 is acquired as the entire image, the area of the empty portion (B region) is large, so the empty portion becomes the model candidate region. However, in the region surrounded by A and the region surrounded by B in FIG. 6, the lightness and saturation are different even for the same blue color. Therefore, if the entire sky portion is used as a model image, even if the user is paying attention to the dark sky blue portion, the dark sky blue portion and the light sky blue portion are averaged. For this reason, it is necessary to further specify a model area.

  FIG. 7 shows a flowchart of the model feature amount determination process.

  First, in S31, the entire image is divided (see FIG. 8). In the example shown in FIG. 8, the entire image is divided into six parts, but it may be divided into four parts, eight parts, or the like.

  Next, in S32, the feature amount in the specific hue region is extracted for each divided image. As the feature quantity to be extracted, representative values of the S value and the V value in the specific hue region can be given, but other feature quantities may be used. When the representative value of the S value and the V value is used as the feature value, two feature values are extracted for each divided region.

  In S33, the feature quantities extracted for each divided area in S32 are sorted and displayed. FIGS. 9A and 9B show examples of display modes on the display means 6. In FIG. 9A, the representative value of the acquired S value is displayed as saturation. The coordinate values of the scales 30A to 30F in FIG. 9A correspond to the representative values of the S values extracted for each divided region. Therefore, when the representative values of the S values of the divided areas are biased, the scales may be also biased as shown in FIG.

  In FIG. 9B, the representative value of the acquired V value is displayed as brightness. The coordinate values of the scales 40A to 40F in FIG. 9B correspond to the representative values of the V values extracted for each divided region. Therefore, when the representative values of the S values of the divided areas are biased, the scales may be also biased as shown in FIG.

  The user can select a desired feature amount by operating the cursor 20 displayed on the display means 6 using the operation panel 4 and / or the touch panel. Further, the scale may be displayed with a number, and a desired number may be directly input.

  In S34, the divided area corresponding to the feature amount selected in S33 is set as a model image.

  Returning to FIG. In S25, a first feature amount is extracted from the determined model image. Specifically, a region of a specific hue is extracted from the model image, and a representative value of H value, a representative value of S value, and a representative value of V value in the extracted region are extracted. Note that the representative value of the S value and the representative value of the V value do not need to be calculated again when the values already acquired in S32 of FIG. 7 are stored in the RAM 7 or the like.

  Returning to FIG. In S14, the pixels of the original image are converted to HSV. Since this process is the same as S12, a description thereof will be omitted.

  Next, the process for extracting the second feature amount in S15 in FIG. 4 will be described with reference to FIG. FIG. 11 is a flowchart of the second feature amount extraction process.

  In S41, a region of a specific hue is extracted.

In S42, the region of the specific hue is extracted from the original image, and the ratio of the representative value and the region of the specific hue in the original image in the specific hue is acquired. When the specific hue is B (blue), it is defined as follows. The same processing is performed when the other color has a specific hue.
・ Representative value of B area: iHb, iSb, iVb

Further, the ratio of the area B to the original image is defined as follows.
-Ratio of B area in original image: iRateB

  Next, details of the process of correcting the original image based on the first feature value and the second feature value, which are executed in S16 of FIG. 4, will be described. This process is performed by converting the H value, S value, and V value of each pixel of the original image.

  The H ′ value after the conversion of the B area can be obtained by the following equation, assuming that the minimum value in the B area set in S21 is Hmin and the maximum value in the B area is Hmax.

When H <iH,
H ′ = Hmin + (sHb−Hmin) × (H−Hmin) ÷ (iHb−Hmin)

When H> iH,
H ′ = sHb + (Hmax−sHb) × (H−iHb) ÷ (Hmax−iHb)

  By using this equation, only the region to be converted can be converted.

  Here, in the conversion of the H value, a hue correction table as shown in FIG. 12 can be used. FIG. 12 is a hue correction table in the case where only the B region is to be converted. In the hue correction table shown in FIG. 12, since the H values other than the B area are H ′ = H, image correction is not performed.

Next, conversion in the S value and the V value will be described. The S value and the Y value are converted for each area divided by the H value. For example,
When S ≦ iSb,
S ′ = S × (sSb ÷ iSb) (Formula 1)
When S> iSb,
S ′ = 1 + (S−1) × {(1−sSb) ÷ (1−iSb)} (Formula 2)
When V ≦ iVb,
V ′ = V × (sVb ÷ iVb) (Formula 3)
When V> iVb,
V ′ = 1 + (V−1) × {(1−sVb) ÷ (1−iVb)} (Formula 4)
It can be calculated by the following formula.
In the following, the conversion table defined by the S value conversion formula may be referred to as a saturation correction table, and the conversion table defined by the V value conversion formula may be referred to as a brightness correction table. .

  Next, a conversion formula for converting into a format (for example, RGB value) suitable for the printer 10 in S17 of FIG. 4 will be described. In addition, the conversion formula shown below is an example, and it cannot be overemphasized that you may convert with another conversion formula.

(In, fl, m, and n shown below are parameters used in the process of calculating RGB from HSV)
Let in be the integer part of (H ÷ 60) and fl be the decimal part of (H ÷ 60).
If in is an even number
fl = 1-fl
m = V × (1-S)
n = V × (1-S × fl)
When in is 0
R = V × 255
G = n × 255
B = m × 255
When in is 1
R = n × 255
G = V × 255
B = m × 255
When in is 2
R = m × 255
G = V × 255
B = n × 255
When in is 3
R = m × 255
G = n × 255
B = V × 255
When in is 4
R = n × 255
G = m × 255
B = V × 255
When in is 5
R = V × 255
G = m × 255
B = n × 255
Is converted as

  As described above, according to the present invention, a color area to be a model is determined from the entire image, and a desired color is determined by the user, so that the image correction process desired by the user can be performed with high accuracy. In addition, by performing the above-described image correction processing, it is possible to correct the hue of the original image to the hue of the model image for each region divided based on the H value.

[Image Correction Processing (Second Embodiment)]
In the H value conversion process described above, since the H value of only a specific hue is converted, there is a possibility that a pseudo contour (tone jump) may be generated between the image-corrected region and the image-uncorrected region. That is, when expressed as a hue correction table, it is as shown in FIG. The hue correction table shown in FIG. 12 is obtained by taking the representative value of the H value of the original image as “H” on the X axis and taking the representative value of the H value of the model area as “H ′” on the Y axis. When the occurrence of gradation skip is not preferable, the following image correction process may be performed on the H value. In the following description, the case where the B area is set to the specific hue will be described. However, the process is basically the same when the other area is set to the specific hue.

H ′ = (y2−y1) ÷ (x2−x1) × H− (y2−y1) ÷ (x2−x1) × x2 + y2
... (Formula 5)
Here, x1, x2, y1, and y2 are defined as follows.

When iHc ≦ H <iHb,
(X1, y1) = (iHc, sHc)
(X2, y2) = (iHb, sHb)

When iHb ≦ H <iHm,
(X1, y1) = (iHb, sHb)
(X2, y2) = (iHm, sHm)

  In this case, the relationship between H and H 'can also be expressed as a hue correction table as shown in FIG. In this hue correction table, the H value representative value (iHc = 180, sHc = 180) in the C region adjacent to the B region in the color space and the H value representative value (iHb, sHb) in the B region are linear. In addition, the representative value of the H value (iHm = 300, sHm = 300) in the M region adjacent to the B region in the color space and the representative value of the H value in the B region (iHb, sHb) are connected by a straight line. It will be.

  Therefore, the C region where the H value is 180 <H ≦ 210 and the M region where the H value is 270 <H ≦ 300 are also converted. This conversion amount becomes larger as the value is closer to the B region.

  As described above, in the second embodiment of the image correction processing, even if the region is not the conversion target, the H value adjacent in the color space is partially converted. It is possible to prevent the generation of a pseudo contour (gradation skip) between a non-target region.

[Image Correction Processing (Third Embodiment)]
In the second embodiment of the image correction process, since a correction curve (conversion formula) is independently used for each region with respect to the S value and the V value, a pseudo contour (tone jump) may be generated. . That is, as shown in FIG. 14, each region has a table indicating the relationship between S and S ′, and no consideration is given to the nature of the table in adjacent regions.

  In the present embodiment, as shown in FIG. 15, it is possible to prevent gradation skip by smoothing the correction curve in each color region.

  Specific processing of this embodiment will be described below. The image correction process for a part of the area B will be described with reference to FIGS. 16 and 17, but the contents of the process are basically the same for the other areas.

The corrected S value (Sb ″) in this embodiment is
-H value (H) of the conversion target area,
-H value representative value (Hbmid) of the area to be converted,
A representative value (Hcmid) of the H value in the adjacent region (in this case, the C region) closer to the hue coordinate position of the H value of the pixel to be converted;
-S value (corresponding to Sb ', Hbmid) of the conversion target area converted by the conversion formula corresponding to the above formula (1) (that is, calculated using the saturation correction table of the B area),
-S value of the adjacent area (that corresponds to Sc ', Hcid) converted by the conversion formula corresponding to the above formula (2) (that is, calculated using the saturation correction table of the C area),
Can be obtained by the following equation.

Sb ″ = {(H−Hcmid) × Sb ′ + (Hbmid−H) × Sc ′} ÷
{(Hbmid−Hcmid)} (Formula 6)

Further, the corrected V value (Vb ″) in the present embodiment is
-H value (H) of the conversion target area,
-H value representative value (Hbmid) of the area to be converted,
A representative value (Hcmid) of the H value in the adjacent region (in this case, the C region) closer to the hue coordinate position of the H value of the pixel to be converted;
-V value (corresponding to Vb ′, Hbmid) of the conversion target area converted by the conversion formula corresponding to the above formula (3) (that is, calculated using the brightness correction table of the B area),
-S value of the adjacent area (that corresponds to Vc ′, Hcmid) converted by the conversion formula corresponding to the above formula (4) (that is, calculated using the brightness correction table of the C area),
Can be obtained by the following equation.

Vb ″ = {(H−Hcmid) × Vb ′ + (Hbmid−H) × Vc ′} ÷
{(Hbmid−Hcmid)} (Expression 7)

  The above-described processing is performed for a part of the B region (H value range: 210 <H ≦ 240) and a part of the C region (H value range 180 <H ≦ 210) shown in FIG. As a result, the output saturation value (S ″) and lightness value (V ″) are obtained by weighting calculation according to the input hue value (H), thereby smoothing the correction effect between the hues. Can do.

  Further, the present invention can be realized by appropriately combining the above-described image correction processes. That is, it is possible to make the image correction stand out in H, and to prevent gradation skipping in S and V.

  The number of divided areas “6” given as an example of the present invention is the number of vertices excluding white and black, which are achromatic colors, from the RGB space (number of vertices 8) that is one of the color gamuts expressing colors. It is the same as the number “6”. In order for a person to identify a color, six is sufficient as the number of color regions, and if it is less than six, the user may feel that it has not been converted like a model image. If the data is divided more finely than 6, the conversion accuracy becomes higher. However, there is a high possibility that the person cannot be identified, and the amount of calculation increases as the number of divisions increases. In the case of using a printer, the time until obtaining an image to be corrected as a printing result is delayed, and there is a high possibility that user dissatisfaction will increase. Therefore, it is considered that the number of divided areas is preferably 6.

  As described above, the present invention performs an image correction operation in a simple and sensuous manner by performing image correction on an image input from a storage medium so as to have the characteristics of the image input from the scanner. It can be performed.

  Further, according to the image correction processing of the present invention, for example, when it is desired to correct the sky blue to the bright sea blue with respect to the original image in which the building and the sky are reflected as the original image, You can correct the blue color of the original image to the bright blue of the ocean by reading the photo with the scanner.

  Also, if you want to brighten the skin color in an image showing a human face, you can correct the skin color of the original image to a light skin color by reading a picture of the palm that has moved to a bright skin color with a scanner. .

  Further, detailed parameters such as dark colors and light colors can be selected by indirect and sensory operations, and image correction processing according to the user's wishes can be performed.

  Since the user does not need to input parameters or the like, he / she does not need any specialized knowledge and can perform desired image correction only by reading an image serving as a model for image correction.

  In addition, this invention is not limited to the said embodiment, Of course, various improvement and deformation | transformation are possible within the range which does not deviate from the summary of this invention. For example, instead of reading a model image from a scanner, the image can be read as a model image from a storage medium. Further, instead of reading the original image from the storage medium, the original image can be read from the scanner. For example, a multifunction machine equipped with a scanner and a printer is described as an embodiment as an image processing apparatus. However, the present invention is not limited to this. For example, an information processing apparatus such as a personal computer performs image correction, and the information processing apparatus. The printer may be configured to perform printing processing.

  The present invention can also be realized as an image processing method for executing the above-described processing. Further, the present invention can be realized as a program for causing the computer to execute the image processing method and a recording medium on which the program is recorded.

  Further, the above-described flowchart is merely an example, and the process may be realized by another flowchart as long as it can obtain a result equivalent to the above-described process.

  Furthermore, not only can the corrected original image be printed, but it can also be stored in a storage medium or transmitted to another device.

It is the figure which showed the external appearance of the image processing apparatus. It is the figure which showed the internal structure of the image processing apparatus. It is a figure showing a user's operation and a flow of basic processing of the image processing apparatus. It is a flowchart of the basic processing in 1st Embodiment of this invention. It is a flowchart of a hue specific process. It is the figure which showed an example of the whole image. It is a flowchart of a model image determination process. It is the figure which showed an example of the divided | segmented whole image. It is the figure which showed the example of a display of a display means. It is the figure which showed the example of a display of a display means. It is a flowchart of the 2nd feature-value extraction process. It is the figure which showed the hue correction table. It is the figure which showed the hue correction table. It is the figure which showed the saturation correction table. It is the figure which showed the change of the curve of saturation correction | amendment. It is the figure which showed the saturation correction table in B area | region and C area | region. It is the figure which showed the corrected S value in B area | region and C area | region. It is the figure which showed that a part of B area | region and C area | region became conversion object.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Image processing apparatus 2 Scanner 3 Memory slot 4 Operation panel 5 Ejection port 6 Display means 10 Printer

Claims (10)

In an image processing apparatus comprising a first image input means, a second image input means, a hue identification means, a designated area determination means, a feature amount extraction means, and an image correction means,
The first image input means inputs a first image,
The second image input means inputs a second image,
The hue specifying means specifies a hue component having the largest component ratio among the hue components constituting the first image as a specific hue,
The designated area determining means includes:
Dividing the first image into a plurality of regions;
For each of the plurality of regions, extract the region of the specific hue,
For each of the plurality of regions, a display feature amount is extracted from the extracted region,
Information on a plurality of display feature amounts corresponding to each of the plurality of regions is displayed on a display unit,
Let the user select one display feature value from the display feature values displayed on the display unit;
Determining a divided region corresponding to the one display feature amount as a designated region;
The feature amount extraction unit extracts a first feature amount based on a designated area in the first image, and extracts a second feature amount based on the second image;
The image correcting means corrects the second image based on the first feature amount and the second feature amount;
An image processing apparatus. Of the areas divided by the designated area determining means, the display feature amount corresponding to a divided area in which the specific hue area in the divided area occupies a predetermined amount or less is excluded from the display target. ,
The image processing apparatus according to claim 1. Correction data creating means for creating correction data based on the first feature quantity and the second feature quantity;
The image correcting means corrects the second image based on the correction data;
The image processing apparatus according to claim 1, wherein the image processing apparatus is an image processing apparatus. The feature amount extraction means includes:
Extracting a feature quantity related to hue from each of the first image and the second image;
Dividing each of the first image and the second image into a plurality of regions based on the feature amount relating to the hue;
Extract features other than hue for each divided area,
The correction data creating means includes
For each of the divided areas of the first image and the second image, a representative value of the feature value related to the hue is extracted, and hue correction data is created based on the representative value,
For each of the divided areas of the first image and the second image, extract a representative value of the feature amount related to other than the hue,
The image correcting means includes
Correcting a feature amount related to a hue for each pixel in the second image based on the hue correction data;
Correcting a feature amount related to other than the hue for each pixel of the second image based on a representative value for each region;
The image processing apparatus according to claim 3. The hue specifying means specifies the hue based on the H value in the HSV space;
The image processing apparatus according to claim 1, wherein the image processing apparatus is an image processing apparatus. The display feature amount is a feature amount related to lightness and / or saturation.
The image processing apparatus according to claim 1, wherein the image processing apparatus is an image processing apparatus. The first image input means inputs an image optically read from a print medium set at a predetermined reading position as a first image,
The second image input means inputs image information stored in a print medium as a second image;
An image processing apparatus according to any one of claims 1 to 6. A printing control unit;
The print control means performs a process for printing the corrected second image information.
The image processing apparatus according to claim 1, wherein the image processing apparatus is an image processing apparatus. A first image input step for inputting a first image;
A second image input step for inputting a second image;
A hue specifying step for specifying a hue component having the largest component ratio among hue components constituting the first image as a specific hue;
A dividing step of dividing the first image into a plurality of regions;
A specific hue extraction step for extracting the specific hue area for each of the plurality of areas;
A display feature amount extraction step for extracting a display feature amount from the extracted region for each of the plurality of regions;
A display step of displaying information on a plurality of display feature amounts corresponding to each of the plurality of regions on a display unit;
A feature amount selection step for allowing a user to select one display feature amount from the display feature amount displayed on the display unit;
A designated area determining step for determining a divided area corresponding to the one display feature amount as a designated area;
A first feature amount extracting step of extracting a first feature amount based on a designated region in the first image;
A second feature amount extracting step for extracting a second feature amount based on the second image;
An image correction step of correcting the second image based on the first feature amount and the second feature amount;
An image processing method comprising: On the computer,
A first image input step for inputting a first image;
A second image input step for inputting a second image;
A hue specifying step for specifying a hue component having the largest component ratio among hue components constituting the first image as a specific hue;
A dividing step of dividing the first image into a plurality of regions;
A specific hue extraction step for extracting the specific hue area for each of the plurality of areas;
A display feature amount extraction step for extracting a display feature amount from the extracted region for each of the plurality of regions;
A display step of displaying information on a plurality of display feature amounts corresponding to each of the plurality of regions on a display unit;
A feature amount selection step for allowing a user to select one display feature amount from the display feature amount displayed on the display unit;
A designated area determining step for determining a divided area corresponding to the one display feature amount as a designated area;
A first feature amount extracting step of extracting a first feature amount based on a designated region in the first image;
A second feature amount extracting step for extracting a second feature amount based on the second image;
An image correction step of correcting the second image based on the first feature amount and the second feature amount;
An image processing program for executing