JP2002140700A - Method and device for processing color image - Google Patents

Abstract

PROBLEM TO BE SOLVED: To automatically reduce information volume disappearing at the time of outputting color picture data generated by an electronic image pickup device to a monitor or a printer. SOLUTION: A color image processor is provided with a color type conversion means 3 for generating a lightness image of hue/saturation/lightness space regulated by a cylindrical coordinate system from a primary color image, an area division means 6 for analyzing the texture of the lightness image and dividing the lightness image into plural areas on the basis of the analytical result, a density conversion curve preparation means 7 for preparing the density conversion curve of the lightness image by smoothing the histogram of each divided area, a density conversion means 8 for converting the density of the lightness image by using the density conversion curve, and a color type reverse conversion means 5 for generating a primary color image by using the lightness image whose density is converted.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a color image processing method and apparatus suitable for an electronic image pickup apparatus such as a video camera and a digital camera, and more particularly to a method and apparatus for improving a dynamic range.

[0002]

2. Description of the Related Art A scene in which a very bright part and a very dark part are mixed, for example, a face of a person with a window in the room is taken by an electronic image pickup device, and the image is displayed on a monitor or a printer. Output, a very bright portion jumps or a very dark portion is crushed, and a phenomenon occurs in which detailed information that would have been obtained by the image sensor of the electronic imaging device cannot be reproduced. This is because the dynamic range of an image output device such as a monitor or a printer is narrower than the dynamic range of an image output from an electronic imaging device.

Conventionally, various methods have been proposed to reduce the loss of detailed information as described above. For example, Japanese Patent No. 2951909 discloses that an input image is divided into a plurality of square lattice blocks, an average luminance of each block is calculated, and tone correction is performed for each of the divided areas based on the average luminance. A gradation correction device and a gradation correction method for an image pickup apparatus are disclosed.

However, in the above-mentioned Japanese Patent No. 2951909, since the area division of the input image is performed based on the average luminance of the blocks, for example, the luminance is stepped in a narrow luminance range as shown in FIG. Is adjacent to the texture A whose luminance changes stepwise within a wide range of luminance width as shown in FIG. The gradation correction is performed using the gradation correction curve. For this reason, when the gradation correction curve is optimally set for texture A, there is a possibility that a very bright portion of texture B will jump or a very dark portion will be crushed. Further, when the gradation correction curve is optimally set for texture B, there is a possibility that the stepwise luminance change of texture A may not be reproduced.

Therefore, the inventors of the present invention first provided a dynamic range automatic compression method for dividing an input image into a plurality of regions and performing different density conversion processing for each of the regions.
An automatic dynamic range compression method characterized by analyzing the texture of an input image and determining the area based on the analysis result has been proposed (Japanese Patent Application No. 33/1999).
No. 3205). According to the dynamic range automatic compression method, there is an effect that loss of useful information can be reduced as compared with the conventional method.

[0006]

SUMMARY OF THE INVENTION The present invention provides a method and an apparatus for applying the above dynamic range automatic compression method to a color image.

[0007]

According to the present invention, there is provided a color image processing method comprising the steps of: generating a lightness image in a hue / saturation / lightness space standardized by a cylindrical coordinate system from a color primary color image; Analyzing the texture of the image, dividing the brightness image into a plurality of regions based on the analysis result, and performing a density conversion of the brightness image by smoothing a histogram for each of the divided regions; Generating a color primary color image using the brightness image subjected to the density conversion. With this configuration, by performing the dynamic range improvement processing on the lightness value, the same improvement processing can be simultaneously performed on the saturation value.

When smoothing the histogram, a density histogram is created for each of the regions, and a first clip for determining the degree of smoothing of the histogram based on the density variation of each region. It is characterized by defining a value. With this configuration, even if the density histogram includes a high-frequency density portion, excessive contrast enhancement can be prevented.

Further, the first clip value is determined by using the complexity of the texture of each region in addition to the degree of density variation of each region. With this configuration,
Excessive contrast emphasis processing for a region including an object with a small density difference can be prevented.

[0010] A second clip value for weakening the degree of histogram smoothing of each area and uniformly changing the brightness of the entire area is used in combination with the first clip value. With this configuration, the degree of contrast enhancement can be reduced and the brightness of the entire region can be made uniform.

Further, the color image processing apparatus of the present invention comprises: means for generating a lightness image standardized in a cylindrical coordinate system from a color primary color image; analyzing a texture of the lightness image; Means for dividing the brightness image into a plurality of regions; means for performing density conversion of the brightness image by smoothing a histogram for each of the divided regions; and color conversion using the brightness image subjected to the density conversion. Means for generating a primary color image. With this configuration, by performing the dynamic range improvement processing on the lightness value, the same improvement processing can be simultaneously performed on the saturation value.

[0012]

Embodiments of the present invention will be described below in detail with reference to the drawings.

(First Embodiment) FIG. 1 shows a first embodiment of the present invention.
FIG. 2 is a block diagram illustrating a configuration of the imaging apparatus according to the embodiment, and FIG. 2 is a flowchart illustrating an outline of a processing flow in the imaging apparatus.

As shown in FIG. 1, the imaging apparatus 1 includes an imaging unit 2, a color format conversion unit 3, a dynamic range improvement processing unit 4, and a color format inverse conversion unit 5.

The image pickup means 2 includes a semiconductor image pickup device such as a CCD. The image pickup means 2 picks up an object, generates RGB primary color image information, and outputs it to the color format conversion means 3.

The color format conversion means 3 performs the processing in step S1 of FIG.
As shown in (1), the RGB primary color image information is converted into hue (H: hue), saturation (S: saturation),
The image information is converted into image information in an HSI space including intensity (I).

There are various models for associating the RGB space and the HSI space. Here, conversion is performed using a bi-hexagonal pyramid color model. The concept of the hexagonal pyramid color model will be described with reference to FIG. (A) of this figure shows the setting of the lightness axis in the RGB space, (b) shows the state of projection on a plane perpendicular to the lightness axis, and (c) shows the HSI space of the di-hexagonal pyramid after projection. Show.

In each pixel of the color image, the HSI value is calculated from the RGB values by using the above-mentioned hexagonal pyramid color model. The brightness information (brightness value) represents a component of the brightness of the color image, and matches the monochrome image information generated from the color image information.

Then, in the first embodiment of the present invention,
As shown in FIG. 4, an HSI space in which a bi-hexagonal pyramid color model is standardized in a cylindrical coordinate system is employed, and is used for RGB → HSI conversion and HSI → RGB conversion. Hereinafter, R
An example of an algorithm of the GB → HSI conversion and the HSI → RGB conversion will be described.

Considering a cube contacting three axes in the RGB orthogonal coordinate system, R, G, B and their complementary colors C, M, Y have a positional relationship as shown in FIG. The main diagonal axis of the RGB cube is a lightness axis I, one vertex is black at I = 0, the other vertex is white at I = 1, and I = [(max {R, G, B} + (min ｛R, G,
B｝] / 2.

Here, when an RGB cube is projected in parallel onto a plane orthogonal to the I axis, a regular hexagon as shown in FIG. 3B is formed. On the other hand, the hue H and the saturation S are defined as shown in FIG. 3C on a plane orthogonal to the I axis. Hereinafter, the conversion method will be described. Where R, G, B, S, I
Is [0, 1], and H has a value of [0, 2π].

[1] RGB → HSI Conversion First, I is defined by the following equation. _{I = (I max + I min} ) / 2

Where I _max = max {R, G, B} I _min = min {R, G, B}

I) When I _max = I _min S = 0 H = undefined

Ii) When I _max ≠ I _min S is defined as follows. When I ≦ 0.5: S = (I _max −I _min ) / (I _max
+ I _min ) I> O. When _{5: S = (I max -I} min) / (2-I
_max + I _min )

Next, r, g, and b are determined as follows. _{r = (I max -R) /} (I max -I min) g = (I max -G) / (I max -I min) b = (I max -B) / (I max -I min)

Finally, H is determined as follows. When R = I _max : H = π / 3 (b−g) When G = I _max : H = π / 3 (2 + r−b) When B = I _max : H = π / 3 (4 + g−r) )

[2] HSI → RGB Conversion First, M ₁ and M ₂ are obtained as follows. When I ≦ 0.5: M ₂ = I · (1 + S) I> O. At 5: M ₂ = I + S−I · S M ₁ = 2I−M ₂

I) When S = 0 R = G = B = 1

Ii) When S ≠ 0: Processing # 1 Assuming h = H + (2/3) π, processing # 2 described below is performed, and R is determined as follows using the obtained value X. R = X

The processing # 2 described later is performed with h = H, and G is determined as follows using the obtained value X. G = X

Assuming that h = H- (2/3) π, a process # 2 described later is performed, and B is determined as follows using the obtained value X. B = X

[0033]: defining the processing # 2 First h _a in the following manner. <time of _{0: h a = h + 2π} h> h a = h h time of 2π: h _a = h-2π

[0034] then determined as follows: X by the value of h _a. h _a <π / 3 when _{X = M 1 + (M 2} -M 1) · h a / (π / 3)

[0035] _{(π / 3) ≦ h a} < time of π X = M ₂

The π ≦ h _a <(4/3) when _{π X = M 1 + (M} 2 -M 1) · ((4/3) π-h a /
(Π / 3))

[0037] (4/3) when _{π ≦ h a <2π X =} M 1

As described above, the HS standardized in the cylindrical coordinate system
When the I space is used, only the dynamic range compression is performed on the brightness information, and the same correction can be simultaneously performed on the saturation information (saturation value). This will be described with reference to FIG. FIG. 5 is a diagram showing the correspondence between a plane passing through the I-axis and the S-axis of the HSI space standardized by the cylindrical coordinate system and the bi-hexagonal pyramid color model. As shown in this figure, when the lightness value is converted from I ₁ to I ₂ in the HSI space standardized by the cylindrical coordinate system, in the bi-hexagonal pyramid color model, the lightness value changes and the saturation value also becomes S _1.
It is converted to S ₂ from.

The brightness value I _i output from the color format conversion means 3
Is input to the dynamic range improvement processing unit 4. Dynamic range improvement processing unit 4, as shown in step S2 of FIG. 2 performs a predetermined dynamic range improvement process on the brightness value I _i.

As shown in FIG. 1, the dynamic range improvement processing section 4 comprises a region dividing means 6, a density conversion curve creating means 7, and a density conversion means 8. Then, as shown in FIG. 6, the density conversion curve creation means 7 includes a density histogram creation means 11, an entropy calculation means 12,
Edge degree calculation means 13, contrast operation clip value determination means 14, density shift operation clip value determination means 15, first
It comprises a clipping means 16, a second clipping means 17, and a cumulative histogram creating means 18.

The area dividing means 6 performs steps S21 to S21 in FIG.
As shown in S22, region division and spatial quantization of region boundaries are performed on the input brightness image. First,
In step S21, the texture of the input image is analyzed, and the input image is divided into a plurality of regions based on the analysis result. In the first embodiment of the present invention, a region is determined by a filtering process of calculating a LOG (Laplacian Of Gaussian) filter shown in the following equation [1] on an input image. FIG. 8 shows the result of region division of the image shown in FIG.
(B).

[0042]

(Equation 1)

Here, the result of region segmentation by texture analysis often differs from the result of manual region segmentation by a human looking at the same image. Therefore, if the result of the area division by the above-described texture analysis is used as it is, a part where both results are different seems unnatural to human eyes.

Therefore, in the first embodiment of the present invention,
In the next step S22, the result of the area division analyzed on a pixel-by-pixel basis is quantized with a slight reduction in resolution.
Specifically, as shown in FIG. 8 (c), the input image is divided into square blocks, which are superimposed on the result of the region division shown in FIG. 8 (b). , To which region it belongs is determined according to the occupancy of the region. As a result, the result of the area division shown in FIG. 8B is quantized as shown in FIG.

The image data spatially quantized by the area dividing means 6 is input to the density histogram creating means 11 in the density conversion curve creating means 7. Density histogram creation means
At 11, a density histogram is created for each region as shown in step S23 of FIG.

The density conversion curve generating means 7 basically generates a density histogram for each area, then generates a cumulative histogram for each area, and performs density conversion using the cumulative histogram. However, only by this basic processing, for example, a cumulative histogram shown in FIG. 9B is created from the density histogram shown in FIG.
_{When the} density is converted from the output density _i0 to the output density _Io0 , the value of the cumulative histogram changes abruptly in a high-density portion of the density histogram, so that the contrast is excessively emphasized.

Therefore, in the first embodiment of the present invention,
By introducing the first clip value, excessive contrast enhancement is prevented. That is, as shown in FIG. 10A, a first clip value CL is set for the density histogram, and a portion above the first clip value CL is clipped and extracted. Then, as shown in FIG. 10B, the sum of the clipped portions is averaged and arranged below the original density histogram. That is, the average value of the sum of the histograms of the portion equal to or larger than the first clip value CL is the bias value of the density histogram. Curve A in FIG. 10 (c)
₁ is a cumulative histogram created from the previous density histogram performing clip, curve A ₂ is a cumulative histogram generated from the density histogram after clipping. As is apparent from the comparison between the two curves, the clipping eliminates a sudden change in the value of the cumulative histogram. Therefore, a situation where the contrast is excessively enhanced is prevented. Here, if the first clip value CL is large, the degree of smoothing of the density histogram is large, and the first clip value CL is large.
Is small, the degree of smoothing of the density histogram is small.

Here, as shown in FIG. 11 (a), for a region where the density value of the density histogram already has a large variation, in other words, for a region having a wide dynamic range, the image is output by an image output device having a narrow dynamic range. Since the detailed information hardly disappears, the necessity to smooth the density histogram is small. On the other hand, as shown in FIG. 11B, in a region where the density value of the density histogram is small, that is, in a region having a narrow dynamic range, when output is performed by an image output device having a narrow dynamic range, there is a high possibility that detailed information is lost. Therefore, it is necessary to expand the dynamic range by smoothing the density histogram.

Therefore, in the first embodiment of the present invention,
The degree of variation of the density value of the density histogram is obtained for each area, and the clip value CL that determines the degree of smoothing of the density histogram is determined according to the degree of variation. Specifically, the entropy calculating means 12 calculates the entropy H using the following equation [2], and obtains the first clip value CL using the characteristic shown in FIG.

[0050]

(Equation 2)

At this time, in the case of an area including an object originally having a small density difference, such as a sky or a white wall, a clip value which is a degree of smoothing of the histogram is determined only by the degree of variation of the density value of the density histogram. , Excessive processing may be performed. Therefore, in the first embodiment of the present invention, the clip value is re-determined in consideration of the complexity of the texture.

As an index indicating the complexity of the texture,
Here, an edge extraction result obtained by calculation with a LOG filter proposed as one method of region division is used. When a filtering process is performed to calculate an LOG filter having two large and small σ values (spatial constants) on an image,
Different edge extraction images are obtained depending on the magnitude of the value.
FIG. 12 shows an example of the result. In the figures (a) and (b), the area surrounded by the bold line in the upper right part indicates the sky.
As is clear from this figure, the difference in the number of edges of the image in the area, such as the sky, where there is no density difference as seen by a human, regardless of the magnitude of the σ value, is small. Therefore, this feature is adopted as a value representing the complexity of the texture, and the clip value is re-determined by the following equation [3], and the degree of smoothing is reduced in an area having a small difference in the number of edges.

[0053]

(Equation 3)

In this equation, CL _old is a clip value determined based only on the degree of density variation within the area, CL _new is a newly determined clip value, and edge _small and edge _large are areas where the σ value is small and large, respectively. Is the number of edges in. Here, edge _small and edge _large are edge degree calculating means 13
, And CL _new is calculated by the contrast operation clip value determination means 14. Then, the clip is executed by the first clipping means 16.

Since the above-described histogram smoothing performed for each region is originally a method of contrast enhancement in image processing, an unnatural result may be produced depending on the type of texture. An example is human skin. In particular, when applied to a color image, the color difference becomes remarkable, and the result becomes conspicuous. Therefore, the first embodiment of the present invention employs a technique for correcting the brightness of a region in order to weaken the degree of smoothing of the histogram, that is, the degree of contrast enhancement, and to prevent loss of information occurring at the time of output. Introduce a second clip value.

The second clip value is set below the density histogram. As shown in FIG. 13A, for all density values of the density histogram, the second clip value CL
Remove the parts below _{2 and} sum the sum to the minimum density value (0)
Alternatively, it is added to the frequency of the maximum density value (255).
FIG. 13B shows a case where the sum is added to the minimum density value. Then, using the density histogram thus clipped, a cumulative histogram is created, and a normalized histogram is used as a density conversion curve. Curve A ₃ in FIG. 13 (c) is a cumulative histogram created from the previous density histogram performing clip, curve A ₄ is a cumulative histogram generated from the density histogram after clipping. As can be seen from the shape of the density conversion curve, by introducing the second clip value, it is possible to obtain a result in which the degree of contrast enhancement is reduced and the brightness of the entire region is uniformly changed. Here, the second clip value is determined by the density shift operation clip value determining means 15,
The clip is executed by the second clipping means 17.
It is preferable that the second clip value has a proportional relationship with the first clip value.

Then, a cumulative histogram is created by the cumulative histogram creating means 18 using the density histograms clipped by the first clipping means 16 and the second clipping means 17. The operations from the entropy calculating means 12 to the first clipping means 16 and the second clipping means 17 correspond to step S24 in FIG. 7, and the operation of the cumulative histogram creating means 18 corresponds to step S25.
Corresponding to

The density conversion curve created by the cumulative histogram creation means 18 is output to the density conversion means 8. As shown in step S26, the density conversion means 8 performs density conversion of each pixel in the area using a different cumulative histogram for each area as a density conversion curve. However, in order to prevent the boundary of the region from becoming discontinuous, the following linear interpolation processing is performed on each pixel in the square block corresponding to the boundary.

As shown in FIG. 14A, the density value of the target pixel is determined by using the density conversion curves of the block B ₁ to which this pixel belongs and three blocks B ₂ , B ₃ and B _{4 in} the vicinity. Density conversion and density values g ₁ , g ₂ , g after conversion
_3, get the g _4. Next, the density value g (x, y) after linear interpolation is calculated based on the following equation [4]. That is, the density values g ₁ , g ₂ , g ₃ , and g ₄ are divided into four blocks B
Weighting is performed according to the distance from the center of ₁ , B ₂ , B ₃ , and B ₄ to the pixel of interest.

[0060]

(Equation 4)

The brightness image information _Io1 subjected to the density conversion processing output from the density conversion means 8 is input to the color format inverse conversion means 5. The color format inverse converter 5 uses the lightness image information _Io1 and the hue information H and the saturation information S output from the color format converter 3 as shown in step S4 in FIG. Of the RGB primary color image information. This color image information is input to a printer or monitor (not shown) and printed or displayed.

(Second Embodiment) The second embodiment of the present invention improves the visibility of an image by expanding the dynamic range of saturation by bringing the lightness value closer to the center level of the lightness range. The feature is to make it.

FIG. 15 is a block diagram showing a configuration of an imaging apparatus according to the second embodiment of the present invention, and FIG. 16 is a flowchart showing an outline of a processing flow in the imaging apparatus.
In these drawings, the same or corresponding components as those in FIG. 1 or FIG. 2 are denoted by the same reference numerals as those used in those drawings.

In the second embodiment of the present invention, as shown in FIG. 15, a saturation emphasizing processing section 21 and a selector 22 are provided, and as shown in step S3 of FIG. In this case, the brightness information processing is performed, and other configurations and operations are the same as those of the above-described first embodiment of the present invention.

The brightness information processing for correcting the saturation information is to add the following processing to the brightness value in order to widen the dynamic range of the color vividness information. When the saturation value has a certain magnitude, that is, as the distance from the I axis in the cylindrical coordinate system in FIG. 4 increases, a process of moving the lightness value in a direction in which the dynamic range of the saturation value becomes wider is performed. . By this process, when the saturation value is large to some extent, the vividness of the color can be more easily understood,
The visibility of an image can be improved. Here, the method for expanding the dynamic range of the saturation value is described in FIG.
As shown in FIG. 7, this is a direction in which the lightness value approaches の of the maximum value (128 when the lightness value range is 0 to 255). Specifically, when I <128, for each pixel, the saturation information processing unit 21 uses the following equation [5].
If I ≧ 128, the brightness value is calculated by the following equation [6].

I _o2 = I _i + (S × Δ _max ) Equation [5]

I _o2 = I _i − (S × _Δmax ) Equation [6]

In these equations, I _o2 is the lightness value after movement, I _i is the original lightness value, S is the saturation value, and _Δmax is the movement maximum value. Therefore, Expressions [5] and [6] are obtained by multiplying the saturation value S having a value of 0 to 1 by the moving maximum value _Δmax, and adding the values when the original lightness value is smaller than 128. When it is big, it means pulling. FIG.
An example of 128 is shown.

Equation [5] calculated by the saturation emphasis processing section 21
_{Alternatively} , the brightness value I _o2 of [6] is input to the selector 22 together with the brightness value I _o1 which has been subjected to the same processing as that of the first embodiment by the dynamic range improvement processing section 4 and output. The value closer to 128 is adopted as the lightness value _Io3 of the pixel, and is input to the color format inverse conversion means 5.

As described above, according to the second embodiment of the present invention, in addition to the effects of the first embodiment, when the saturation value is large to some extent, it becomes easier to understand the vividness of the color. Thus, the visibility of the image can be improved.

The present invention is not limited to the above-described embodiment, but can be variously modified. For example, in the first embodiment, the RGB → H
In order to reduce the time required for SI conversion and HSI → RG conversion, processing may be performed as described in (1) to (3) below.

(1) The brightness value I is calculated from the RGB values, and at the same time, the RGB values are stored. (2) Perform dynamic range improvement processing for the lightness value.

(3) Based on the RGB values stored in (1),
Recalculate the RGB color image. That is, the difference is that the saturation information and the hue information are not calculated. The processed color image data is the same as in the first embodiment.

[0074]

As described above in detail, according to the color image processing method and processing apparatus of the present invention, the dynamic range is improved with respect to the lightness value of the hue / saturation / lightness space standardized by the cylindrical coordinate system. By performing the processing, the same improvement processing can be simultaneously performed on the saturation value.

Further, according to the color image processing method of the present invention, by introducing the first clip value, it is possible to prevent excessive contrast enhancement even if the density histogram includes a high-frequency density portion. it can.

Further, according to the color image processing method of the present invention, the first clip value is determined in consideration of the complexity of the texture of each area, whereby excessive contrast with respect to an area including an object having a small density difference is obtained. Emphasis processing can be prevented.

According to the color image processing method of the present invention, by introducing the second clip value, the degree of contrast enhancement can be reduced and the brightness of the entire area can be made uniform.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a configuration of an imaging device according to a first embodiment of the present invention;

FIG. 2 is a flowchart showing an outline of a flow of color image processing in the imaging apparatus of FIG. 1;

FIG. 3 is a diagram for explaining a bi-hexagonal pyramid color model;

FIG. 4 is a diagram for explaining an HSI space standardized by a cylindrical coordinate system;

FIG. 5 is a diagram showing a correspondence relationship between an HSI space standardized in a cylindrical coordinate system and a bi-hexagonal color model;

FIG. 6 is a block diagram showing a configuration of a density conversion curve creating unit in the imaging device of FIG. 1;

7 is a flowchart showing an outline of a processing flow of a dynamic range improvement processing unit in the imaging device of FIG. 1;

FIG. 8 is a diagram for explaining an operation of a region dividing unit in the dynamic range improvement processing unit in FIG. 1;

FIG. 9 is a diagram for explaining smoothing of a histogram;

FIG. 10 is a diagram for explaining a first clip value;

FIG. 11 is a diagram for explaining a method of determining a first clip value;

FIG. 12 is a diagram showing an example of acquiring edge extracted images having different σ values;

FIG. 13 is a diagram for explaining a second clip value;

FIG. 14 is a diagram for explaining a linear interpolation method in a density conversion unit;

FIG. 15 is a block diagram illustrating a configuration of an imaging device according to a second embodiment of the present invention;

16 is a flowchart showing an outline of the flow of color image processing in the imaging device in FIG. 15;

17 is a diagram for explaining brightness information processing for correcting saturation information in the imaging device in FIG. 15;

FIG. 18 is a view for explaining a method for realizing the brightness information processing in FIG. 17;

FIG. 19 is a diagram for explaining a conventional gradation correction method.

[Explanation of symbols]

 Reference Signs List 3 color format conversion means 4 dynamic range improvement processing unit 5 color format reverse conversion means 6 area division means 7 density conversion curve creation means 8 density conversion means

 ────────────────────────────────────────────────── ─── Continuing on the front page (72) Inventor Satoko Imai 3-1-1-1, Hiyoshi, Kohoku-ku, Yokohama-shi, Kanagawa F-term in Keio University Faculty of Science and Engineering F-term (reference) 5B057 CA01 CA08 CA12 CB01 CB08 CB12 CC02 CE05 CE17 CH18 DA08 DB02 DB06 DB09 DC23 DC25 5C066 AA01 CA07 EA03 EA11 EA19 EC01 EE04 GA01 KA08 KE05 KM02 5C077 LL19 MP08 PP02 PP14 PP15 PP21 PP31 PP32 PP35 PP37 PP47 PQ19 TT09 5C079 HB01 HB06 HB11 LA12 MA15 LA39 LB00

Claims (5)

[Claims] Generating a lightness image in a hue / saturation / lightness space standardized by a cylindrical coordinate system from a color primary color image; analyzing a texture of the lightness image; Dividing the brightness image into a plurality of regions; performing a density conversion of the brightness image by smoothing a histogram for each of the divided regions; and a color primary color using the brightness converted image. Generating a color image. 2. When smoothing the histogram,
2. The color according to claim 1, wherein a density histogram is created for each of the regions, and a first clip value for determining a degree of smoothing of the histogram is determined based on a density variation of each region. Image processing method. 3. The color image processing method according to claim 2, wherein the first clip value is determined using the complexity of the texture of each area in addition to the degree of variation in the density of each area. 4. The method according to claim 1, wherein a second clip value for weakening the degree of histogram smoothing of each area and uniformly changing the brightness of the entire area is used in combination with the first clip value. 3. The color image processing method according to item 2. 5. A means for generating a lightness image in a hue / saturation / brightness space standardized in a cylindrical coordinate system from a color primary color image, analyzing a texture of the lightness image, and Means for dividing the brightness image into a plurality of areas; means for performing density conversion of the brightness image by smoothing a histogram for each of the divided areas; and color primary colors using the brightness image subjected to the density conversion. Means for generating an image.