JP2002135587A - Image processing apparatus and its method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To overcome the problem of a conventional image processing unit that has obtained a correction result of an image where a portrait part gets very dark because the conventional image processing unit discriminates it to be overexposure when correcting the image with an entirely high luminance level such as a photo in which a person is photographed against a background of a white wall so as to apply proper correction only to the white wall part. SOLUTION: A mask data/weight data generating section 14 acquires image attribute information from a user via an instruction input section 13, reads an image detection condition on the basis of the image attribute from a storage section to generate mask data. A histogram generating section 6 generates a histogram on the basis of image data and mask data stored in an image buffer 3. A lookup table generating section 7 calculates a parameter required for correction on the basis of the generated histogram to generate a lookup table. The mask data/weight data generating section 14 generates weight data on the basis of the mask data. An image correction section 9 uses the generated lookup table and the weight data to correct the image data stored in the image buffer 3.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing apparatus and method, and more particularly, to an image processing apparatus and method for performing gradation correction on image data.

[0002]

2. Description of the Related Art In an image processing apparatus for processing a multi-valued image, it is possible to obtain a clearer image by adjusting the brightness value of the brightest highlight portion or the darkest shadow portion in the image. The target white balance adjustment is performed. In such an image processing apparatus, when adjusting the white balance, in the image, in a high-luminance region which is a range of several% from the higher luminance,
The average value of the RGB values of the pixels excluding the pixels whose luminance is equal to or more than a predetermined value is calculated, and each pixel is corrected based on the average value.

[0003]

Problems may occur when the image is corrected regardless of the scene of the target image, that is, without considering the characteristics of the image. For example, when correcting an image having a high luminance level as a whole, such as a photograph in which a person is photographed against a white wall, the image processing apparatus determines that the image is overexposed and performs appropriate correction on the white wall portion. , A dark correction result is obtained.

An object of the present invention is to solve the above-mentioned problem, and an object of the present invention is to perform correction according to a designated area on an image to image data.

It is another object of the present invention to perform correction on image data corresponding to an area corresponding to attribute information of an image and other areas.

[0006]

The present invention has the following arrangement as one means for achieving the above object.

An image processing apparatus according to the present invention acquires designation means for accepting designation of an arbitrary region in a displayed image, and acquires a luminance distribution of image data corresponding to the image and a luminance distribution of the designated region. Acquiring means, based on the acquired luminance distribution, first correction data for correcting the image data, second correction data for correcting the image data of the designated area, and the first and second Generating means for generating third correction data obtained by weighted averaging of the two correction data, generating means for generating weight data corresponding to each pixel of the image data, and information indicating the designated area and the weight data And correcting means for correcting the image data by using any of the first to third correction data.

A setting unit for setting an image detection condition based on attribute information of the image; an obtaining unit for obtaining a luminance distribution of image data corresponding to the image and a luminance distribution of a region matching the image detection condition; And, based on the obtained luminance distribution, first correction data for correcting the image data, second correction data for correcting the image data of the match area, and the first and second Generating means for generating third correction data weighted average of the correction data, creating means for creating weight data corresponding to each pixel of the image data, based on the information indicating the match area and the weight data, Correction means for correcting the image data by using any one of the first to third correction data.

An image processing method according to the present invention receives a designation of an arbitrary region in a displayed image, acquires a luminance distribution of image data corresponding to the image, and acquires a luminance distribution of the designated region. A first correction data for correcting the image data, a second correction data for correcting the image data of the designated area,
And generating third weighted data obtained by weighted averaging the first and second correction data, creating weight data corresponding to each pixel of the image data, and generating information indicating the designated area and the weight data. The image data is corrected using any one of the first to third correction data.

[0010] An image detection condition is set based on the attribute information of the image, and a luminance distribution of image data corresponding to the image and a luminance distribution of an area matching the image detection condition are obtained. Based on the distribution, first correction data for correcting the image data, second correction data for correcting the image data of the match area,
In addition, a third correction data is generated by weighted averaging the first and second correction data, weight data corresponding to each pixel of the image data is created, and information indicating the match area and the weight data are generated. The image data is corrected using any one of the first to third correction data.

[0011]

DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, an image processing apparatus according to an embodiment of the present invention will be described in detail with reference to the drawings.

An image processing apparatus according to the present embodiment described below performs image correction using a plurality of lookup tables. The image processing apparatus of the present embodiment supplies or incorporates the image processing program into a computer device such as a personal computer, various scanners, image input devices such as digital still cameras and digital video cameras, and various printers. It is realized by. That is, software and firmware in which the functions of the present embodiment described below are incorporated in an image editing program and a driver program constitute the present invention.

[0013]

First Embodiment [Configuration] FIG. 1 is a block diagram showing an example of a functional configuration of an image correction device for performing image processing according to a first embodiment.

In FIG. 1, an image input unit 2 reads image data to be subjected to image correction from an input image holding unit 1,
The image data is written to the image buffer 3.

A user interface (UI) unit 12 displays the contents of the image buffer 3 on a monitor (not shown) or the like, acquires area data designated by a user (hereinafter, referred to as "mask data"), and Write to. Therefore, the image buffer 3 holds image data, mask data, and weight data described later.

The histogram creating section 6 creates a histogram of the entire image data stored in the image buffer 3 and an area designated by the mask data (hereinafter referred to as a "mask area"), and stores the result in a histogram. Part 4
To be stored.

The look-up table creation unit 7 creates a lookup table by calculating parameters required for correction based on the histogram stored in the histogram storage unit 4, and stores the result in the lookup table storage unit 5. I do.

The weight data creating section 8 creates weight data based on the mask data, and stores the result in the image buffer 3.

The image correction unit 9 corrects the image data stored in the image buffer 3 using the look-up table stored in the look-up table holding unit 6 and the weight data stored in the image buffer 3. Are stored in the image buffer 3 again.

The image output unit 10 reads out image data from the image buffer 3 and writes the image data into the output image holding unit 11.

Although not shown in the figure, of the above configuration, the image input unit 2, the histogram creation unit 6, the look-up table creation unit 7, the weight data creation unit 8, the image correction unit 9, the image output unit 10, and the UI The unit 12 and the like are configured by a work memory such as a CPU and a RAM and a program. Further, it is preferable that the image buffer 3, the histogram storage unit 4 and the look-up table storage unit 5 are allocated to a memory such as a RAM or a hard disk.

[User Interface] FIG. 2 is a diagram showing an example of a user interface provided by the UI unit 12, which includes an image display unit, an end button, and a cancel button. In addition, a section image indicating a section in a state where the image is divided into blocks is overlay-displayed on the image display unit.

Although the details will be described later, the user specifies a desired block by clicking a block in the image display unit using a pointing device such as a mouse, and then clicks an end button to specify the block. finish. Clicking the cancel button cancels the designation process.

The UI unit 12 stores mask data indicating the clicked block in the image buffer.

The number of blocks is not limited to 5 × 3 shown in FIG. 2, but may be 7 × 5 or a block divided more finely, or the number of blocks may be changed adaptively in consideration of the aspect ratio of the image. May be. Of course, since it is desirable to set the number of blocks according to the characteristics of the image to be corrected, the block size is "enlarged" and "reduced" as shown in FIG.
Buttons can be provided to allow the user to set an appropriate block size.

The shape of the block is not limited to a rectangle.
It may be triangular, hexagonal or trapezoidal.

[Image Correction Procedure] FIG. 3 is a flowchart showing an outline of an image correction procedure in the first embodiment.

In step S101, the image input unit 2 reads image data from the input image holding unit 1 and stores it in the image buffer 3.

In step S102, the UI unit 12 displays an image represented by the image data stored in the image buffer 3 on the interface shown in FIG.

In step S103, an operation input to the interface shown in FIG. 2 is waited. If there is an operation input and the block is clicked, the process proceeds to step S104. If the end button is pressed, the process proceeds to step S106, and the process is canceled. If the button has been pressed, the process ends.

When the block is clicked, step S4
Then, the mask data is stored in the image buffer 3, and the histogram holding unit 4 is initialized. Subsequently, step S105
After the image data stored in the image buffer 3 is subjected to the image correction described below, the process returns to step S102.

When the end button is pressed, the step
In S106, the image data is read from the image buffer 3 by the image output unit 10 and written into the output image holding unit 11, whereby the corrected image data is output.

[Image Correction Processing] FIG. 4 is a flowchart showing details of the image correction processing (step S105).

In step S2, a histogram creation unit
6 creates a histogram of the image data and the mask area stored in the image buffer 3 and stores the result in the histogram holding unit 4.

In step S3, the look-up table creating unit 7 calculates parameters necessary for correction based on the histogram stored in the histogram holding unit 4, creates a look-up table, and compares the result with the look-up table. It is stored in the holding unit 5.

In step S4, the weight data creating unit 8
Creates weight data based on the mask data, and stores the result in the image buffer 3.

In step S5, the image correction unit 9 uses the look-up table stored in the look-up table holding unit 5 and the weight data stored in the image buffer 3 to store the image data stored in the image buffer 3. Is corrected and stored in the image buffer 3 again.

[Creation of Histogram] FIG. 5 shows a histogram creation process (step S) in the histogram creation unit 6.
It is a flowchart which shows an example of 2).

In step S11, one pixel of image data and mask data is extracted from the image buffer 3. It is assumed that the image buffer 3 stores the luminance data of each of the RGB colors.

In step S12, the luminance L of the pixel is obtained from the RGB values according to the following equation. L = (3 x R + 6 x G + 1 x B) / 10

In step S13, the histogram stored in the histogram holding unit 4 is updated. The histogram holding unit 4 stores a histogram Hi of the calculated luminance L.
The histograms HistR, HistG, and HistB that accumulate stL and RGB values for each luminance L of the pixel are held. Furthermore, a histogram HistL of the luminance L of the mask area
Msk is also stored in the histogram storage unit 4. Note that these histograms are all zero in the initial state of the creation start. The histogram is updated according to the following equation. HistR [L] = HistR [L] + R HistG [L] = HistG [L] + G HistB [L] = HistB [L] + B HistL [L] = HistL [L] + 1

In step S14, it is determined from the mask data whether or not the pixel is included in the mask area. If the pixel is in the mask area, in step S15, the histogram HistL of the mask area stored in the histogram holding unit 4 is determined.
Update Msk according to the formula below. HistLMsk [L] = HistLMsk [L] + 1

In step S16, it is determined whether or not the processing for all pixels has been completed. If the processing has not been completed, the flow returns to step S11.

FIG. 6 is a diagram showing an example of the histogram HistL created in this way.

[Calculation of Parameters] FIG. 7 is a flowchart showing an example of a parameter calculation process (step S3) in the lookup table creation unit 10.

In step S21, the maximum luminance of the image is obtained from the histogram HistL stored in the histogram holding unit 4. In the histogram example shown in FIG. 6, the maximum luminance is 252.

In step S22, a predetermined amount (for example, 10) is subtracted from the luminance 255, and a luminance LH 'at which the obtained maximum luminance becomes larger is obtained. Specifically, 2
The value is reduced to 55, 245, 235,..., And each time the value is compared with the obtained maximum luminance. Therefore, in FIG. 6, LH ′ = 245
It is. Subsequently, on both sides of LH ′, a region (a highlight region indicated by hatching in FIG. 6) including a predetermined percentage (for example, 1% of the total number of pixels) of pixels is obtained, and the minimum luminance of the region is determined by a highlight point LH. (LH = 234 in FIG. 6).

Then, in step S23, according to the following equation,
Average RGB value in the highlight area where the luminance is LH or more and LH 'or less
Calculate RH, GH and BH. ^{_{RH = Σ m = LH LH '}} HistR [m] / Σ m = LH LH' HistL [m] GH = Σ m = LH LH 'HistG [m] / Σ m = LH LH' HistL [m] BH = Σ m _{= LH} ^{LH '} HistB [m] / Σ _{m = LH} ^LH' HistL [m]

In step S24, the minimum luminance of the image is obtained from the histogram HistL stored in the histogram holding unit 4. In the histogram example shown in FIG. 6, the minimum luminance is 5.

In step S25, a predetermined amount (for example, 10) is added from the luminance 0, and a luminance LS 'at which the obtained minimum luminance becomes smaller is obtained. Specifically,
The value is increased to 0, 10, 20,..., And each time the value is compared with the obtained minimum luminance. Therefore, in FIG. 6, LS ′ = 10. Subsequently, on both sides of LS ′, an area (shadow area shown by hatching in FIG. 6) including a predetermined percentage (for example, 1% of the total number of pixels) of pixels is obtained, and the maximum luminance of the area is determined by the shadow point LS (FIG. LS = 22 in 6).

Then, in step S26, according to the following equation,
Average value R of RGB in the shadow area where the luminance is LS 'or more and LS or less
Calculate S, GS and BS. RS = Σ _{m = LS '} ^LS HistR [m] / Σ _{m = LS'} ^LS HistL [m] GS = Σ _{m = LS '} ^LS HistG [m] / Σ _{m = LS'} ^LS HistL [m] BS = Σ _{m = LS '} ^LS HistB [m] / Σ _{m = LS'} ^LS HistL [m]

In step S27, the obtained RH, GH, B
The lookup tables LUT _R , LUT _G, and LUT _B for RGB are created from H, RS, GS, and BS. FIG. 8 is a diagram showing an example of the created lookup table. Then, the creation result is stored in the lookup table holding unit 5.

In step S28, the luminance LH _{Tmp is calculated by} the following equation.
And LS _Tmp are obtained, and a lookup table LUT _Tmp for HistL correction is created from the result. LH _Tmp = (RH × 3 + GH × 6 + BH × 1) / 10 LS _Tmp = (RS × 3 + GS × 6 + BS × 1) / 10

In step S29, His using the LUT _Tmp
Correct tL and HistLMsk. These are used later when calculating the exposure correction amount.

In step S30, the average luminance of the input image is calculated from the corrected HistL and HistLMsk, and the exposure correction look-up tables LUT _L and LU are determined according to a predetermined method.
Create T _L Msk. Figure 9 shows LUT _L and LUT _L Msk created
It is a figure showing an example of. Then, the creation result is stored in the lookup table holding unit 5.

[Lookup Table] Lookup tables LUT _R , LUT _G and LUT _B shown in FIG. 8 are used to correct contrast and color cast.

The gamma characteristics of highlights are established in the order of G, B, and R. Thus, G and B for R
Can be corrected, for example, the color cast of an image having a bluish color cast can be corrected. At the same time, contrast can be corrected.

On the other hand, the lookup table LUT shown in FIG.
_L is a look-up table for optimally correcting the exposure other than the mask area. The look-up table LUT _L Msk is a look-up table for optimally correcting the exposure of the mask area. That is, the brightness of the entire image can be appropriately adjusted using the two lookup tables.

[Creation of Weight Data] FIG. 10 is a flowchart showing an example of the weight data creation processing (step S4) in the weight data creation unit 8.

In step S31, a variable i used as a counter is initialized to zero, and an array Wg of weight data corresponding to each pixel of the image data stored in the image buffer 3.
Initialize tImg [] to all zeros. However, when the pixel corresponding to MskImg [0] is in the mask area, WgtImg [0] = 10.

In step S32, it is checked whether or not the pixel corresponding to MskImg [i] is in the mask area. If the pixel is in the mask area, the process proceeds to step S33. If not, the process proceeds to step S39.

If the pixel corresponding to MskImg [i] is in the mask area, the value of WgtImg [i] is checked in step S33,
If [i] <5, in step S34, according to rule 1 described later
Update WgtImg []. If WgtImg [i] ≧ 5, the value of WgtImg [i] is checked in step S37, and if WgtImg [i] = 10, WgtImg [i + 1] = 10 in step S38.

On the other hand, if the pixel corresponding to MskImg [i] is outside the mask area, the value of WgtImg [j] is checked in step S39, and if WgtImg [i]> 5, the process proceeds to step S40, where rule 2 WgtImg [] is updated according to.

Thereafter, in step S35, the counter i is incremented. In step S36, it is determined whether or not the processing of all pixels (all weight data) has been completed.
Return to 2.

[Weight Data Update Rule] FIG. 11 is a flowchart showing an example of weight data update rule 1. Note that the variable w shown in FIG. 11 is an index value indicating the target array.

In step S51, the larger of WgtImg [w-4] and 1 is set to the value of WgtImg [w-4]. Similarly, in steps S52 to S54, the larger value of WgtImg [w-3] and 2 is set to the value of WgtImg [w-3], and the larger value of WgtImg [w-2] and 3 is set. To the value of WgtImg [w-2], the value of WgtImg [w-1] and 4
Is set to the value of WgtImg [w-1].

Next, in steps S55 to S60, WgtImg [w] =
5, WgtImg [w + 1] = 6, WgtImg [w + 2] = 7, WgtImg [w + 3] = 8, Wg
Set tImg [w + 4] = 9 and WgtImg [w + 5] = 10.

FIG. 12 is a flowchart showing an example of the weight data update rule 2.

In step S61, the smaller of the value of WgtImg [w-5] and 9 is set to the value of WgtImg [w-5]. Similarly, in steps S62 to S64, the smaller value of WgtImg [w-4] and 8 is set to the value of WgtImg [w-4], and the smaller value of WgtImg [w-3] and 7 is set. To the value of WgtImg [w-3], and the value of WgtImg [w-2]
Is set to the smaller value of WgtImg [w-2].

Next, in steps S65 to S70, WgtImg [w-
1] = 5, WgtImg [w] = 4, WgtImg [w + 1] = 3, WgtImg [w + 2] = 2, W
gtImg [w + 3] = 1 and WgtImg [w + 4] = 0.

When the index values w-5 to w + 5 are outside the image data, the processing is not performed.

[Example of Creating Weight Data] FIG. 13 is a diagram showing an example of creating weight data when the weighted average is not considered.

FIG. 13A shows mask data, and shows that the 7th to 12th pixels and the 14th to 19th pixels are included in the mask area. FIG. 12B shows weight data corresponding to the mask data. In this example, since the weighted average is not considered, for example, the weight data corresponding to the pixels in the mask area is 10, and the weight data corresponding to the pixels outside the mask area is 0.

FIG. 14 is a diagram showing an example of creating weight data when performing weighted averaging.

FIG. 14 (a) shows mask data, and FIGS. 14 (b) to 14 (g) show changes in weight data when weight data is created in consideration of the weighted average.

For MskImg [0] to MskImg [6], since the corresponding pixel is outside the mask area, steps S32 → S39 → S35 shown in FIG.
→ Each step of S36 is executed, and their values remain zero (FIG. 14 (b)).

In MskImg [7], since the corresponding pixel is in the mask area and its value is zero, steps S32 → S shown in FIG.
Steps 33 → S34 → S35 → S36 are executed, and the values of WgtImg [3] to WgtImg [12] are updated according to rule 1 (FIG. 14).
(c)).

MskImg [8] to MskImg [11] indicate that the corresponding pixel is in the mask area and their values are all greater than 5 and 10
, So steps S32 → S33 → S37 → S35 → shown in FIG.
Each step of S36 is executed, and their values are not changed (FIG. 14 (c)).

In MskImg [12], since the corresponding pixel is in the mask area and its value is 10, the step S32 → S shown in FIG.
Each step of 33 → S37 → S38 → S35 → S36 is executed and WgtI
10 is substituted for mg [13] (FIG. 14 (d)).

In MskImg [13], since the corresponding pixel is outside the mask area and its value is larger than 5, the step S shown in FIG.
Each step of 32 → S39 → S40 → S35 → S36 is executed, and the value of WskImg [10] is updated from MgtImg [17] according to rule 2 (FIG. 14 (e)).

In MskImg [14], since the corresponding pixel is in the mask area and its value is smaller than 5, the step S32 shown in FIG.
→ Each step of S33 → S34 → S35 → S36 is executed and rules
According to 1, the value of WgtImg [14] is updated from WgtImg [19] (FIG. 14 (f)).

MskImg [15] to MskImg [18] indicate that the corresponding pixel is outside the mask area and their values are all greater than 5 and 1
Since it is less than 0, steps S32 → S33 → S37 → S35 shown in FIG.
→ Each step of S36 is executed, and their values are not changed (FIG. 14 (f)).

Since the target pixel is outside the mask area and its value is larger than 5, MskImg [19]
The steps of 32 → S39 → S40 → S35 → S36 are executed, and the value of WskImg [23] is updated from WgtImg [17] according to rule 2 (FIG. 14 (g)).

In the case of MskImg [20] to MskImg [25], since the target pixel is outside the mask area and their values are all less than 5, the steps S32 → S39 → S35 → S36 shown in FIG. Run and their values remain unchanged (Figure 14).
(g)).

The above processing is performed on all the pixels in units of rows or columns to create weight data.

[Synthesis of Weight Data] FIG. 15 is a diagram for explaining the synthesis of two weight data created in the row or column direction by the above processing. FIG. 15 (a) shows a mask region,
FIG. 15 (b) is weight data created by scanning in the row direction, and FIG.
5 (c) is weight data created by scanning in the column direction.

In the synthesis of the weight data, the weight data of the same pixel in FIGS. 15B and 15C are compared. If the pixel is in the mask area, the smaller weight data is used. Select data. FIG. 15D shows the result of combining the weight data.

[Image Correction Processing] FIG. 16 is a flowchart showing the image correction processing (step S5) by the image correction section 9.

In step S41, a counter i is initialized to zero,
In step S42, the two lookup tables LUT _L and LUT _L Ms stored in the lookup table holding unit 5
k is weighted by the ratio of i: (10-i) to create a new lookup table LUT _L Tmp (FIG. 17). LUT _L Tmp [n] = {i × LUT _L [n] + (10-i) × LUT _L Msk [n]} / 10 where 0 ≦ n ≦ 255

In step S43, the image data and weight data stored in the image buffer 3 are extracted for one pixel. In step S44, if the value of the weight data matches the value of the counter i, the weighted average is obtained in step S45. LUT _L Tmp, LUT _R ,
After correcting the image data extracted from the image buffer 3 based on LUT _G and LUT _B , the result is stored in the image buffer 3
Overwrite the original location. R = LUT _L Tmp [LUT _R [R]] G = LUT _L Tmp [LUT _G [G]] B = LUT _L Tmp [LUT _B [B]]

Next, based on the determination in step S46, the processes in steps S43 to S45 are performed on all the pixels. When the processing for all the pixels is completed, the counter i is incremented in step S47. If i> 10 in the determination in step S48, the processing is terminated. Otherwise, the processing returns to step S42.

As described above, when an image is corrected using different look-up tables LUT _L and LUT _L Msk inside and outside the mask area, an intermediate characteristic between the two look-up tables near the boundary of the mask area. Use the lookup table LUT _L Tmp with. Furthermore, LUT _L Tmp of the characteristic corresponding to the weight data set for each pixel near the boundary
Is used, the correction characteristic near the boundary can be smoothly changed between the two look tables. Therefore, it is possible to prevent the generation of a pseudo contour near the boundary. For example, in order to apply a desirable correction to the human skin part of an image, to specify a human skin part as a mask area, and to perform a different correction on the human skin part and other parts, to prevent the occurrence of a false contour. Can be.

[0093]

[Modification] FIG. 18 is a flowchart showing a modification of the image correction procedure shown in FIG.

In the above embodiment, although only one block is a target of histogram creation (see FIG. 3), the position of the initialization step of the histogram holding unit 4 can be changed as shown in step S111 of FIG. For example, a plurality of blocks can be designated as a histogram creation target.
Further, since the same block can be designated many times, the weight can be changed for each block.

FIG. 19 is a diagram showing a modification of the user interface shown in FIG.

In the above-described embodiment, an example has been described in which an image divided into blocks is displayed in advance. However, in the user interface shown in FIG. 19, an arbitrary area in the image is designated as a mask area. Can be. In the designation method, the size and shape of the mask area are determined in advance, and the center of the mask area is designated using a mouse cursor or the like.

Further, a histogram may be created with the same specific gravity in the entire mask area. For example, the specific gravity near the center of the mask area is set to 100, the specific gravity near the end of the mask area is set to zero, and the like. A histogram may be created with a specific gravity that decreases toward the end. Further, the specific gravity may be reduced linearly, or the specific gravity may be reduced along a quadratic curve.

[0098]

Second Embodiment Hereinafter, an image processing apparatus according to a second embodiment of the present invention will be described. Note that, in the present embodiment,
Components that are substantially the same as those in the first embodiment are given the same reference numerals, and detailed descriptions thereof are omitted.

[Structure] FIG. 20 is a block diagram showing an example of the functional structure of an image correction apparatus for performing image processing according to the second embodiment.

The image correction apparatus according to the second embodiment includes a mask data / weight data generation unit 14 and an instruction input unit 13 instead of the weight data generation unit 8 and the UI unit 12.
1 is different from the image forming apparatus of the first embodiment shown in FIG. Also,
The mask data / weight data creation unit 14 includes a storage unit such as a ROM in which image detection conditions corresponding to the image attribute information are stored. In the following, data indicating whether a pixel satisfies the image detection condition is referred to as “mask data”.

The image buffer 3 holds image data, mask data and weight data. The histogram holding unit 4 holds a histogram of the entire image data and a histogram of an area satisfying the image detection condition (hereinafter, referred to as a “mask area”).

[Image Correction Procedure] FIG. 21 is a flowchart showing an outline of image correction in the second embodiment.

In step S201, the image input unit 2
The image data to be corrected is read from the input image holding unit 1 and stored in the image buffer 3.

In step S202, the mask data / weight data creation unit 14 acquires image attribute information from the user via the instruction input unit 13, reads image detection conditions based on the image attributes from the storage unit, and reads the mask data. Create it and store the result in the image buffer 3.

In step S203, the histogram creating section 6 creates a histogram based on the image data and the mask data stored in the image buffer 3, and stores the result in the histogram holding section 4.

In step S204, the look-up table creating unit 7 calculates parameters necessary for correction based on the histogram stored in the histogram holding unit 4,
A lookup table is created, and the result is stored in the lookup table holding unit 5.

In step S205, the mask data / weight data creation unit 14 creates weight data based on the mask data, and stores the result in the image buffer 3.

In step S206, the image correcting section 9
The image data stored in the image buffer 3 is corrected using the look-up table stored in the look-up table holding unit 5 and the weight data stored in the image buffer 3, and then stored in the image buffer 3 again.

The processing of steps S203 to S206 is the same as the processing of steps S2 to S5 in the first embodiment (FIG. 4).

Then, in step S 207, the image data is read from the image buffer 3 by the image output unit 10 and written into the output image holding unit 11, so that the corrected image data is output.

[Image Detection Conditions] The image detection conditions are set corresponding to color attributes that are particularly important in color reproduction, such as the color of human skin. FIG. 22 shows an example of an image detection condition corresponding to the color of human skin. FIG. 22 is an xy chromaticity diagram, in which the skin color area is indicated by an ellipse. The horseshoe shape outside the skin color region is a spectrum locus at D65. In other words, if "skin color" is specified as image attribute information,
An image detection condition for extracting a pixel whose xy chromaticity value is within the skin color region in FIG. 22 is read from the storage unit. Note that the image attribute information is not limited to “skin color”, but may be any color (memory color) that is important in color reproduction, such as “sky color”, “ocean color”, “vegetation color”, and the like. The image detection conditions corresponding to those are set and stored in the storage unit. The xy chromaticity value is calculated by the following equation.

FIG. 23 shows a mask data / weight data creating section 14.
9 is a flowchart illustrating an example of a procedure (Step S202) of acquiring an image detection condition according to and generating mask data.

In step S161, the image attribute information designated by the user is acquired from the instruction input unit 13, and in step S162, the image detection condition corresponding to the image attribute information is read from the storage unit. Create mask data indicating pixels that satisfy and pixels that do not.

As described above, an area (mask area) corresponding to the image detection condition corresponding to the attribute information of the image and an area not applicable are set, and the look-up tables LUT _L Msk and LUT _L are used for each area. When performing correction, a lookup table LU having an intermediate characteristic between the two lookup tables near the boundary of the mask area.
Use T _L Tmp. Further, since LUT _L Tmp having characteristics corresponding to the weight data set for each pixel near the boundary is used, the correction characteristics near the boundary can be smoothly changed between the two look tables. Therefore,
It is possible to prevent the generation of a pseudo contour near the boundary.
For example, to apply a desirable correction to the human skin part of the image,
In the case where the human skin portion is designated as the mask region and different corrections are performed on the human skin portion and other portions, it is possible to prevent the occurrence of the pseudo contour.

[0115]

[Modification] In each of the above embodiments, the image data is
Although the description has been made assuming that the digital luminance value ranges from 0 to 255, the maximum value of the data is not limited to 255 and may be not only the luminance but also the dot density.

Also, an example has been shown in which the luminance value is calculated by weighted averaging with a weight of R: G: B = 3: 6: 1. However, the calculation may be performed with other weights, and the maximum value of RGB may be calculated. And the average of the minimum values.

Further, in each of the above embodiments, an example has been described in which a look-up table is created from a histogram of a mask area and a histogram of the entire image. A look-up table may be created from. FIG. 24 is a diagram illustrating an example of a process performed by the histogram creating unit 6 in this case.

That is, in the case of a masked pixel, the histogram HistLUnMsk of the non-mask area stored in the histogram holding unit 4 is updated in step S17. HistLUnMsk [L] = HistLUnMsk [L] + 1

In this case, the lookup table creation unit 7
HistL in steps S29 and S30 is Hi
Corresponds to stLUnMsk.

In each of the above embodiments, the weight data is changed linearly in ten steps at the boundary between the masked area and the non-masked area. Good, and the steps are not limited to 10. Further, the changing method and the number of steps may be changed in the vertical direction and the horizontal direction.

In the calculation method of the xy chromaticity value, XYZ
The coefficient at the time of calculation is not limited to the above. Also, as chromaticity
Although described using the xy value, this is not limited,
The rg chromaticity value obtained from the following equation may be used. Alternatively, the chromaticity value may be determined using an RGB ratio. r = R / (R + G + B) g = G / (R + G + B)

Although the ellipse-shaped image detection condition is shown in FIG. 22, other shapes such as a rectangle may be used.

Further, the image attribute information has been described as being instructed by the user, but may be set in advance.
A means for determining an image attribute may be separately provided. The setting method of the image attribute information is not particularly limited.

[0124]

As described above, according to the present invention,
Correction according to the designated area on the image can be performed on the image data.

Further, it is possible to apply correction to the image data in accordance with each of the area corresponding to the attribute information of the image and the other areas.

[Brief description of the drawings]

FIG. 1 is a block diagram illustrating an example of a functional configuration of an image correction device that performs image processing according to a first embodiment;

FIG. 2 is a diagram showing an example of a user interface provided by a UI unit.

FIG. 3 is a flowchart showing an outline of an image correction procedure according to the first embodiment;

FIG. 4 is a flowchart illustrating details of an image correction process;

FIG. 5 is a flowchart illustrating an example of a histogram creation process in a histogram creation unit.

FIG. 6 is a diagram showing an example of a histogram HistL.

FIG. 7 is a flowchart illustrating an example of a parameter calculation process in a lookup table creation unit;

FIG. 8 is a diagram showing an example of a lookup table.

FIG. 9 is a diagram showing an example of LUT _L and LUT _L Msk.

FIG. 10 is a flowchart illustrating an example of weight data creation processing in a weight data creation unit;

FIG. 11 is a flowchart illustrating an example of a weight data update rule 1;

FIG. 12 is a flowchart showing an example of a weight data update rule 2;

FIG. 13 is a diagram showing an example of creating weight data without considering a weighted average,

FIG. 14 is a diagram showing an example of creating weight data when performing weighted averaging;

FIG. 15 is a diagram for explaining the combination of two weight data created in the row or column direction;

FIG. 16 is a flowchart illustrating an image correction process performed by an image correction unit;

FIG. 17 is a diagram for explaining creation of LUT _L Tmp,

18 is a flowchart showing a modification of the image correction procedure shown in FIG. 3,

FIG. 19 is a view showing a modification of the user interface shown in FIG. 2;

FIG. 20 is a block diagram illustrating an example of a functional configuration of an image correction device that performs image processing according to the second embodiment;

FIG. 21 is a flowchart illustrating an outline of image correction according to the second embodiment;

FIG. 22 is a diagram illustrating an example of an image detection condition.

FIG. 23 is a flowchart illustrating an example of a procedure for acquiring an image detection condition by a mask data / weight data creation unit and creating mask data;

FIG. 24 is a diagram illustrating another example of the process performed by the histogram creating unit.

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Claims (17)

[Claims] 1. A designating unit for accepting designation of an arbitrary region in a displayed image, an acquiring unit acquiring a luminance distribution of image data corresponding to the image, and a luminance distribution of the designated region. Based on a luminance distribution, first correction data for correcting the image data, second correction data for correcting the image data of the specified area, and a weighted average of the first and second correction data Generating means for generating third corrected data, generating means for generating weight data corresponding to each pixel of the image data, and information indicating the designated area and the weight data based on the first to third data. And a correction unit that corrects the image data using any of the correction data. 2. The image processing apparatus according to claim 1, wherein the designation unit has a user interface that displays an image obtained by dividing the block and receives designation of the block as the designated area. 3. The image processing apparatus according to claim 2, wherein the specifying unit can receive a region having a rectangular, circular, or regular polygonal shape as the specified region. 4. The method according to claim 1, wherein the obtaining unit increases a specific gravity near a center of the specified region and decreases a specific gravity near an end of the specified region when obtaining the luminance distribution of the specified region. 4. The image processing device according to claim 1, wherein: 5. The image processing apparatus according to claim 1, wherein the creating unit smoothly changes the value of the weight data near the boundary of the designated area. 6. The image processing apparatus according to claim 1, wherein the creating unit changes the value of the weight data substantially linearly near a boundary of the designated area. . 7. A setting unit for setting an image detection condition based on attribute information of an image, an obtaining unit for obtaining a luminance distribution of image data corresponding to the image, and a luminance distribution of a region matching the image detecting condition. And, based on the obtained luminance distribution, first correction data for correcting the image data, second correction data for correcting the image data of the match area, and the first and second Generating means for generating third correction data obtained by performing weighted averaging of the correction data; generating means for generating weight data corresponding to each pixel of the image data; based on information indicating the match area and the weight data, An image processing apparatus comprising: a correction unit configured to correct the image data by using any one of the first to third correction data. 8. The image processing apparatus according to claim 7, wherein the image detection condition based on the attribute information of the image includes at least a condition for detecting a human skin color region. 9. The image according to claim 8, wherein the correction data corresponding to an area matching a condition for detecting a human skin color area is data for correcting human skin to a preferable color. Processing equipment. 10. The image processing apparatus according to claim 7, wherein the image detection condition based on the attribute information of the image includes at least a condition for detecting a color region based on a memory color of a person. 11. The method according to claim 11, wherein the obtaining unit increases a specific gravity near a center of the specified region and decreases a specific gravity near an end of the specified region when obtaining the luminance distribution of the specified region. 11. The image processing device according to claim 7, wherein: 12. The image processing apparatus according to claim 7, wherein the creating unit smoothly changes the value of the weight data near a boundary of the designated area. 13. The image processing apparatus according to claim 7, wherein the creation unit changes the value of the weight data substantially linearly near a boundary of the designated area. . 14. Receiving designation of an arbitrary region in a displayed image, acquiring a luminance distribution of image data corresponding to the image, and a luminance distribution of the designated region, and based on the acquired luminance distribution, First correction data for correcting the image data, second correction data for correcting the image data of the designated area, and third correction data obtained by weighted averaging the first and second correction data Generating weight data corresponding to each pixel of the image data, based on the information indicating the designated area and the weight data, using any of the first to third correction data, An image processing method comprising correcting image data. 15. An image detection condition is set based on attribute information of an image, and a luminance distribution of image data corresponding to the image and a luminance distribution of a region matching the image detection condition are acquired. Based on the distribution, first correction data for correcting the image data, second correction data for correcting the image data of the match area, and a weighted average of the first and second correction data Generate third correction data, create weight data corresponding to each pixel of the image data, and, based on the information indicating the match area and the weight data, select one of the first to third correction data. Using the image data to correct the image data. 16. A recording medium on which a program code for image processing is recorded, wherein the program code corresponds to at least a code of a step of receiving designation of an arbitrary area in a displayed image, and a code corresponding to the image. A luminance distribution of the image data, and a code of a step of acquiring the luminance distribution of the designated area; first correction data for correcting the image data based on the acquired luminance distribution; image data of the designated area Second correction data for correcting, and a code of a step of generating third correction data by weighted average of the first and second correction data, and weight data corresponding to each pixel of the image data Using any of the first to third correction data based on the code of the step of creating To a recording medium; and a code of the step of correcting the image data. 17. A recording medium on which a program code for image processing is recorded, wherein the program code includes at least a code for setting an image detection condition based on attribute information of an image, and image data corresponding to the image. And a code for a step of obtaining a luminance distribution of a region that matches the image detection condition, based on the obtained luminance distribution, first correction data for correcting the image data, the match region A second correction data for correcting the image data, and a code of a step of generating a third correction data by weighted averaging the first and second correction data, corresponding to each pixel of the image data Based on the code of the step of creating weight data to be executed, and the information indicating the match area and the weight data. Of using any of the correction data, a recording medium; and a code of the step of correcting the image data.