JP2005260675A - Image processor and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image processor and program for adaptively varying a filter coefficient on the basis of a determination result by determining an edge part from luminance and hue in the case of obtaining RGB data from a Bayer's image. <P>SOLUTION: The image processor is provided with: a means for calculating an interpolation value with respect to RGB data of a target pixel from peripheral pixels in the case of obtaining the RGB data from the Bayer's image; a means for calculating the hue and saturation of the target pixel from the calculated data; a means for comparing hues between the target pixel and the peripheral pixels, determining that the target pixel is a false color when a difference between the both hues exceeds a prescribed value and replacing the hue of the target pixel with the peripheral dominant hue; a means for calculating a filter coefficient on the basis of the target pixel and the hue of the peripheral pixels; and a means for outputting the RGB data on the basis of the filter coefficient, thus the image processor adaptively varies the filter coefficient. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention mainly relates to an image processing apparatus and a program suitable for an electronic imaging apparatus such as a digital camera.

  In so-called single-plate CCDs used in digital cameras and digital scanners, the output Bayer image has only one color information per pixel, so RGB data per pixel is obtained by interpolation processing from surrounding pixels. Need to get. However, when uniform interpolation processing is performed on the entire image, problems such as generation of false colors at the edge portions of the image occur.

In order to solve such a problem, a technique has been proposed for suppressing false colors generated near the edge of an image by adaptively changing the filter coefficient of an interpolation filter based on luminance information of surrounding pixels (for example, patents). Reference 1). However, the method of determining the edge of the image based only on the luminance information cannot detect all the edges of the image. This cause is derived from the definition of luminance. For example, the luminance may be the same between bright red and dark green. In this case, the boundary between bright red and dark green is not recognized as an edge, and adaptive filtering is not performed. Therefore, in order to detect all the edges of the image, it is necessary to perform edge determination in consideration of not only the luminance but also the hue of the pixel of interest. There has also been proposed a method for realizing color correction by a simple method from RGB data of a pixel of interest without using complicated processing such as multiplication and division (see, for example, Patent Document 2).
JP 2000-23174 A JP 2001-61160 A

  However, the technique described in Patent Document 1 requires a relatively large circuit to obtain the hue and saturation. In particular, since hue calculation requires an atan calculation process, there is a problem that the circuit load is large and the feasibility is poor. In the technique described in Patent Document 2, it is assumed that RGB data of each pixel is known, and it is not assumed that RGB data per pixel is obtained from a Bayer image by color correction. .

  Therefore, the present invention has been made in view of the above-described problems. When RGB data is obtained from a Bayer image, an edge portion is determined from luminance and hue, and an adaptive filter coefficient is determined based on the determination result. An object of the present invention is to provide an image processing apparatus and a program that can change the above.

The present invention proposes the following means in order to solve the above-described problems.
The invention according to claim 1 is an image processing apparatus that synchronizes a Bayer image input from a single-plate image pickup device by an interpolation unit and outputs it as a color image, and the interpolation unit converts the RGB color image data of the pixel of interest. Temporary interpolation means for calculating the corresponding temporary interpolation value from the RGB color image data of the surrounding pixels, and hue / saturation for calculating the hue and saturation of the pixel of interest from the RGB color image data output from the temporary interpolation means Comparing the hue of the target pixel output from the calculation means and the hue / saturation calculation means with the surrounding pixels, and when the difference in hue between the target pixel and the surrounding pixels exceeds a predetermined value , A false color determination unit that determines that the target pixel is a false color and replaces the hue of the target pixel with a dominant hue around the target pixel, and a hue of the target pixel output from the false color determination unit and its surroundings Pixel A filter coefficient calculating means for calculating the filter coefficients based on the phase, based on the filter coefficients the calculated proposes an image processing apparatus characterized by comprising an output means for outputting the RGB color image data.

  The invention according to claim 4 is a program for use in an image processing apparatus that synchronizes a Bayer image input from a single-plate image sensor by interpolation and outputs it as a color image, and corresponds to RGB color image data of a pixel of interest. A step of calculating a provisional interpolation value from the RGB color image data of the surrounding pixels, a step of calculating hue and saturation from the RGB color image data of the calculated pixel of interest, the calculated pixel of interest and the surrounding pixels thereof And when the difference in hue between the target pixel and its surrounding pixels exceeds a predetermined value, the step of determining that the target pixel is a false color; and When the determination is made, the step of replacing the hue of the pixel of interest with a dominant hue around it, the hue of the pixel of interest replaced or the calculated hue of the pixel of interest Calculating a filter coefficient based on the color information of the surrounding pixels of, based on the filter coefficients the calculated proposes a program for executing the steps of outputting the RGB color image data.

  According to these inventions, simple interpolation processing is executed using the neighboring pixels at the interpolation position by the operation of the temporary interpolation means. The hue / saturation calculation unit calculates the hue and saturation from the RGB image data subjected to the provisional interpolation process. The calculated hue is compared with the hue of the surrounding pixel by the operation of the false color determination means, and when the difference in hue between the pixel of interest and the surrounding pixel exceeds a predetermined value, the pixel of interest has a false color. It is judged that there is, and the hue of the pixel of interest is replaced with the dominant hue around it. The filter coefficient calculation means calculates the filter coefficient based on the hue of the pixel of interest and the hue information of the surrounding pixels, and outputs RGB color image data based on the calculated filter coefficient by the operation of the output means.

  According to a second aspect of the present invention, in the image processing apparatus according to the first aspect, the hue / saturation calculating unit compares the RGB color image data of the pixel of interest, and the order of Max, Mid, Min The pixel of interest is determined by the magnitude relationship between the R, G, and B color image data of the pixel of interest and the magnitude relationship of the difference value between Max and Min and the difference value between Mid and Min. Has proposed an image processing device characterized by calculating the hue of the image.

  According to a fifth aspect of the present invention, in the program according to the fourth aspect, the step of calculating the hue and saturation compares the RGB color image data of the pixel of interest with each other, and labels Max, Mid, and Min. The pixel of interest by the step of adding, the magnitude relationship between the R, G, and B color image data of the pixel of interest, and the magnitude relationship of the difference value between Max and Min and the difference value between Mid and Min. A program characterized by comprising the step of calculating the hue of the image.

  According to these inventions, the hue / saturation calculating means detects the size of the RGB color image data of the pixel of interest, and outputs this as the Max value, the Mid value, and the Min value in the order of the data values. . The output data is developed on the color space coordinates, and the hue is calculated based on the magnitude relationship between the hue area corresponding to the target pixel, the difference value between the Max value and the Min value, and the difference value between the Mid value and the Min value. .

  According to a third aspect of the present invention, in the image processing apparatus according to the first aspect, the false color determination means includes a saturation of the target pixel obtained by the hue / saturation calculation means and a preset saturation. Proposed an image processing apparatus that determines the presence or absence of a false color based on a threshold value of a degree, a sum of absolute values of a difference between a hue of the pixel of interest and a hue of surrounding pixels, and a preset threshold value ing.

  According to a sixth aspect of the invention, in the program described in the fourth aspect, the step of determining the false color is preset with the saturation of the pixel of interest obtained by the step of calculating the hue and saturation. A step of comparing the saturation threshold value, a step of comparing the sum of absolute values of the difference between the hue of the pixel of interest and the hue of the surrounding pixel and a preset threshold value, and the comparison result And a program for determining the presence or absence of a false color.

  According to these inventions, the false color determination means includes the saturation obtained by the hue / saturation calculation means and a preset saturation threshold, and the difference between the hue of the pixel of interest and the hue of its surrounding pixels. The presence or absence of a false color is determined based on the sum of absolute values and a preset hue threshold.

  According to the present invention, the filter coefficient is calculated using not only the edge portion detected from the luminance information but also the edge portion detected from the luminance information and the hue information by a simple hue calculation method using the color space coordinates. By adaptively changing, there is an effect that generation of false colors generated when three Bayer images are made can be efficiently reduced with a simple circuit configuration.

  Hereinafter, an image processing apparatus according to an embodiment of the present invention will be described in detail with reference to FIGS.

  The image processing circuit of the image processing apparatus according to the present embodiment is, for example, a circuit that inputs a Bayer image having a period of 3 × 3 pixels and performs color correction on the Bayer image. As shown in FIG. A temporary interpolation unit 2, a delay unit 3, a hue / saturation calculation unit 4, a false color determination unit 5, a luminance calculation unit 6, a filter coefficient calculation unit 7, and an interpolation unit 8. Yes.

  The preprocessing unit 1 performs preprocessing such as noise reduction processing and white balance adjustment on the input Bayer image. The temporary interpolation unit 2 performs simple interpolation using peripheral pixels located in the vicinity of the target pixel. Specifically, as shown in FIG. 4, when the target pixel is “R”, the RGB color image data of this pixel uses the detected value as it is for the R color image data, and the G color For the image data, the value obtained by multiplying the value detected by the pixel “G” where the pixel of interest is located around the “R” is multiplied by 0.25, and for the B color image data, the pixel of interest A value obtained by multiplying the value detected from the pixel “B” positioned around “R” by a value obtained by multiplying each value by 0.25 is calculated as a temporary interpolation value.

  The delay means 3 applies a certain delay to the Bayer image input to the interpolation means 6 in order to perform final interpolation processing on the Bayer image preprocessed by the preprocessing means 1 using the calculated filter coefficients. To provide synchronization. The hue / saturation calculation unit 4 calculates the hue and saturation from the temporarily interpolated pixel data. Details will be described later.

  The false color determination means 5 determines the presence or absence of a false color by comparing the hue and saturation information input from the hue / saturation calculation means 4 with predetermined threshold values. First, a dominant hue is detected from around the pixel of interest, and the information is output to the filter coefficient calculation means 8. If it is not a false color, the hue of the pixel of interest is output as it is.

  The luminance calculation means 6 receives the RGB color image data temporarily interpolated by the temporary interpolation means 2 and calculates luminance information from these data. The filter coefficient calculation means 7 receives the output signal from the false color determination means 5, the hue information from the hue / saturation calculation means 4 and the luminance information from the luminance calculation means 6, and a filter coefficient for performing color correction. Is calculated. The interpolation unit 8 multiplies the Bayer image input via the delay unit 3 by the filter coefficient output from the filter coefficient calculation unit 7 and outputs an RGB color image.

Next, the hue / saturation calculation means in this embodiment will be described with reference to FIGS. 2 and 10 to 13.
As shown in FIG. 2, the hue / saturation calculation unit 4 includes a size determination unit 41, a saturation calculation unit 42, and a hue calculation unit 43.
Here, before describing each component, the background art as a premise will be described with reference to FIGS. 10 to 12.

  As shown in FIG. 10, considering a color space having three axes of R, G, and B primary colors, reproducible colors are in the range of regular hexagons in the figure. Here, in the figure, point A corresponds to white and point B corresponds to black. When the coordinate axes constituting this color space are rotated so that the points A and B mentioned above intersect, the result is as shown in FIG.

  A color space having three axes of R, G, and B can be considered by converting it to a space having three axes of hue, luminance, and saturation, but regarding hue, “reddish”, “ Since it is only necessary to find out that “it is bluish” or the like, it can be considered by replacing it with the view of which plane in the color space shown in FIG. 11 the color to be reproduced belongs to. For example, if the point C in FIG. 11 is the pixel of interest, the color is determined to be a color in which red is dominant, green is next strong, and there is almost no blue component, that is, “reddish yellow”. it can.

Next, the coordinates of the vertices of the regular hexagon in FIG. 11 are respectively (0, 0, 0), (Rn, 0, 0), (Rn, Bn, 0), (0, Bn, 0), (0, Bn, Gn), (Rn, Bn, Gn), (0, 0, Gn), (Rn, 0, Gn)
The line segment connecting (0, 0, 0) and (Rn, 0, Gn) is R = G, and the line segment connecting (0, 0, 0) and (0, Bn, Gn) is G = B, (0 , 0, 0) and (Rn, Bn, 0) are R = B, and the angles formed by these three line segments and the three axes R, G, and B are 60 degrees.
In addition, when a line segment of R = 2G, R = 2B, G = 2B, G = 2R, B = 2R, and B = 2G is drawn with respect to these line segments, the 360-degree color space becomes 12 regions. Can be divided.

  The hue / saturation calculation means 4 in the present embodiment calculates the hue and saturation in order to determine the false color of the pixel of interest. Here, the false color is, for example, that the surrounding pixels are “yellow It is not necessary to strictly determine the hue of the pixel of interest because the pixel of interest is a “blueish” color when it is “bright” or because the hue changes significantly with respect to surrounding pixels. .

  Further, if the coordinates of the point C as the pixel of interest in FIG. 11 are (R1, G1, B1), the coordinates can also be expressed as (R1-B1, G1-B1, 0). Is used, the calculation of the boundary condition as to whether the point C belongs to which region of the above-mentioned 12-divided hues does not require complicated processing such as multiplication and division. Therefore, the processing load can be greatly reduced.

Next, in order to further simplify the above processing, Max, Mid, and Min are labeled in the descending order of the RGB data of the pixel of interest. For example, as shown in FIG. 12, if the magnitude relationship of the RGB data of the pixel of interest is Max = R, Mid = G, Min = B, as described above, (Max, Mid, Min) is
(Max, Mid, Min) = (Max-Min, Mid-Min, 0)
Can be converted as follows.

  Therefore, if the magnitude relation of the RGB color image data and the conversion coordinates are clarified, first, the hue corresponding to the target pixel is divided into twelve as shown in FIG. 13 from the magnitude relation of the RGB color image data. It is possible to identify which region, and the processing can be further simplified. The relationship between the area of FIG. 13 and the size of the RGB data is shown in Equation 1. In the above example, if there is information that the magnitude relationship of the RGB color image data is the order of R, G, B and the coordinate information of the pixel of interest, this coordinate, the R axis and the line segment R = 2G, R = G By comparing the three boundaries, it is possible to easily identify the corresponding region of the target pixel.

  Therefore, the size determination means 41 in the present embodiment labels and outputs the RGB color image data as Max, Mid, and Min, and sets the determination result of the size relationship of the RGB color image data as T as shown in Equation 2. Output.

  The saturation calculation unit 42 calculates the saturation by obtaining the distance from the origin of the color space coordinates using the values of Max, Mid, and Min input from the magnitude determination unit 41. Of the Max, Mid, and Min values, the (Max-Min) value is considered to be dominant with respect to the distance, and the saturation can be calculated from this value to reduce the processing. The hue calculation unit 43 uses the Max, Mid, Min, and T values input from the size determination unit 41 to filter the pixel to which the pixel of interest belongs among the regions 0 to 11 in FIG. It outputs to the coefficient calculation means 7.

Next, the false color determination means 5 will be described with reference to FIGS.
The false color determination means 5 in this embodiment receives the hue H and saturation S calculated by the hue / saturation calculation means 4 and determines the presence or absence of a false color in the pixel of interest. Specifically, as shown in FIG. 5, when the hue of the pixel of interest calculated by the hue / saturation calculating means 4 is Hc and the hue of the surrounding pixels is Hs0 to Hs7, The sum of the absolute values of the differences between the hue Hc and the hues of the surrounding pixels is calculated, and this is compared with the threshold Th_H shown in FIG. If it is larger than the threshold Th_H, it is considered that a false color has occurred.

  Further, the saturation S is also compared with the threshold Th_S. When the saturation of the pixel of interest is small, there is almost no color, so even if the surroundings and colors are different, the influence is small. Here, a threshold Th_S is set for the saturation, and if it is smaller than the threshold Th_S, it is not regarded as a false color.

  Therefore, when it is determined that the color of the target pixel is higher than a certain color and the hue of the pixel of interest is more than a certain value compared to the hue of the peripheral pixel (shaded area in FIG. 6), Thus, the dominant hue is output as the hue of the pixel of interest. Here, the output Hc ′ indicates the target hue that the pixel of interest should be in this color regardless of whether or not there is a false color.

Next, the filter coefficient calculation unit 7 will be described with reference to FIGS. 3, 7, 8, 9, 14, and 15.
As shown in FIG. 3, the filter coefficient calculation means 7 in the present embodiment includes a hue weight coefficient calculation means 101, a luminance weight coefficient calculation means 102, and a multiplier array 103. The hue weight coefficient calculation unit 101 inputs Hc ′ output from the false color determination unit 5 and H output from the hue / saturation calculation unit 4 and calculates a weight coefficient for the hue.

  Specifically, first, Hc ′ and H are arranged as shown in FIG. Next, when obtaining the weighting coefficient for the R data of the pixel of interest, Hc ′ and the data from H (R1) to H (R4) adjacent to the pixel of interest are compared one by one. The weight coefficients Kh0 to Kh7 are obtained by increasing the weight for data having a small difference from Hc ′ and decreasing the weight for data having a large difference. Such processing is similarly performed on the G color image data and the B color image data.

  In order to obtain 3 × 3 hue information as shown in FIG. 14, peripheral 5 × 5 pixel information is required. As in the present embodiment, when the pixel of interest is an R pixel in a Bayer array, the pixel information of the surrounding 5 × 5 is as shown in FIG. Therefore, for example, when Hc ′ and H (R1) shown in FIG. 14 have similar hues, the R pixels necessary to make H (R1) are the R out of the nine pixels shown in FIG. Since there are only four pixels in the upper left, these weights are increased.

  On the other hand, when H (G2) shown in FIG. 14 has a hue different from Hc ′, the R pixel necessary for making H (G2) is the center of the left column of the R pixels shown in FIG. Since these are the two pixels at the center, these weights are reduced. In this case, the center pixel and the center pixel in the left column correspond to both of the above, but in this case, a weighting factor subtracted by reflecting both effects is set.

  The luminance weight coefficient calculating unit 102 receives luminance information from the luminance calculating unit 6. For example, as shown in FIG. 15, when the luminance of the pixel of interest is high and there are pixels with low luminance in the vicinity, the weighting factor of the high luminance pixel is increased and the weighting factors Ky0 to Ky7 are obtained.

  The multiplier array 103 receives the weighting factors relating to the hues Kh0 to Kh7 output from the hue weighting factor calculating means 101 and the weighting factors relating to the hues Ky0 to Ky7 outputted from the luminance weighting factor calculating means 102. By multiplying these values, coefficients from filter coefficients K0 to K7 as shown in FIG. 9 are output to the interpolation means.

  As shown in FIG. 7, the contributions of the two weighting factors to the filter coefficients are centered on the edges for luminance edges and uniform coefficients for non-edges. As for the hue edge, emphasis is placed on the dominant dominant hue around the edge, and on the non-edge, emphasis is placed on the hue of the pixel of interest.

  The interpolation unit 8 multiplies the Bayer image input from the delay unit 3 by the filter coefficients K0 to K7 input from the filter coefficient calculation unit 7 and outputs a color-corrected RGB color image. The present embodiment can be applied to the case where the odd number filter is used or the case where the even number filter is used.

Next, a processing procedure in the image processing apparatus according to the present embodiment will be described with reference to FIG.
The Bayer image preprocessed in the preprocess 1 is subjected to simple provisional interpolation by using the neighboring pixels of the pixel of interest in the provisional interpolation means 2 (step 101). The RGB signals output from the temporary interpolation unit 2 are input to the hue / saturation calculation unit 4. The input RGB signal is determined in magnitude relation, and it is detected which region of the hue space divided into 12 corresponds to the hue of the pixel of interest. The saturation is approximately calculated using the maximum and minimum of the input RGB signals (step 102).

  The hue and saturation output from the hue / saturation calculation unit 4 are input to the false color determination unit 5, and it is determined whether the hue of the pixel of interest is significantly different from the hues of the surrounding pixels (step 103). If the hue of the pixel of interest is significantly different from the hues of the surrounding pixels, it is determined as a false color, and the hue of the pixel of interest is replaced with the dominant hue of the surrounding (step 104).

  Next, the filter coefficient calculation means 7 compares the hue of this pixel of interest with the surrounding hues, increases the weight for colors similar to this hue, decreases the weight for different colors, and sets the weight coefficient. Calculate (step 105). The calculated weighting coefficient is output to the interpolating means 8 and multiplied by the Bayer image input from the delay means 3 to output an RGB color image (step 106).

  As described above, according to the present embodiment, generation of false colors generated when a single-plate image is converted into a three-plate image by a simple calculation process while considering not only an edge based on luminance but also an edge based on hue. Can be effectively suppressed.

  The embodiments of the present invention have been described in detail with reference to the drawings. However, the specific configuration is not limited to these embodiments, and includes design changes and the like without departing from the scope of the present invention. . For example, in this embodiment, the example in which the hue weighting coefficient is calculated in the hue comparison with Hc ′ has been described. However, the comparison between Hc ′ and Hc is performed, and weighting is performed according to the possibility of false colors. The hue weight coefficient may be calculated by the averaging.

1 is a configuration diagram of an image processing apparatus according to the present invention. It is a block diagram of a hue / saturation calculation means. It is a block diagram of a filter coefficient calculation means. It is a figure which shows the coefficient value of a temporary interpolation process. It is a figure which shows the relationship between a focused pixel and a surrounding pixel. It is a figure which shows the relationship between a threshold value and a false color area | region. It is a figure which shows the contribution with respect to the filter coefficient of a luminance edge and a hue edge. It is a figure which shows the arrangement | sequence of R, G, B in 5x5 pixel surrounding a focused pixel. It is a figure which shows the filter coefficient with respect to the focused pixel of a surrounding pixel. It is a figure which shows the color space which makes RGB three axes. It is a figure which shows the color space which makes RGB 3 axes, and the area | region divided into 12 parts. It is a figure which shows the color space which converted RGB color space into Max, Mid, and Min axis. It is a figure which shows the area | region of the color space divided into 12 parts. It is a figure which shows the relationship of the hue of a focused pixel and a surrounding pixel. It is a figure which shows the relationship between the focused pixel and the brightness | luminance of a surrounding pixel. It is a processing flow figure in a present Example.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 ... Pre-processing means, 2 ... Temporary interpolation means, 3 ... Delay means, 4 ... Hue / saturation calculation means, 5 ... False color determination means, 6 ... Luminance calculation means 7 ... Filter coefficient calculation means, 8 ... Interpolation means, 41 ... Size determination means, 42 ... Saturation calculation means, 43 ... Hue calculation means, 101 ... Hue weight coefficient calculation Means 102: Luminance weight coefficient calculation means 103: Multiplier array

Claims (6)

An image processing device that synchronizes a Bayer image input from a single-plate image sensor by an interpolation unit and outputs a color image,
The interpolating means calculates a temporary interpolation value corresponding to the RGB color image data of the pixel of interest from the RGB color image data of the surrounding pixels;
Hue / saturation calculating means for calculating the hue and saturation of the pixel of interest from the RGB color image data output from the temporary interpolation means;
When the hue of the target pixel output from the hue / saturation calculation unit and its surrounding pixels are compared, and the hue difference between the target pixel and its peripheral pixels exceeds a predetermined value, the target pixel False color determination means for determining that the pixel is a false color and replacing the hue of the pixel of interest with a dominant hue around the pixel;
Filter coefficient calculation means for calculating a filter coefficient based on the hue of the pixel of interest output from the false color determination means and the hue of the surrounding pixels;
An image processing apparatus comprising output means for outputting RGB color image data based on the calculated filter coefficients.   The hue / saturation calculating unit compares the RGB color image data of the pixel of interest, and performs labeling with Max, Mid, and Min in descending order, and R, G, and B color image data of the pixel of interest. 2. The image processing apparatus according to claim 1, wherein the hue of the pixel of interest is calculated based on a magnitude relationship between the difference value between Max and Min and a magnitude relationship between the difference value between Mid and Min. .   The false color determination means includes the saturation of the pixel of interest obtained by the hue / saturation calculation means and a preset saturation threshold, and the difference between the hue of the pixel of interest and the hue of surrounding pixels. The image processing apparatus according to claim 1, wherein presence or absence of a false color is determined based on a sum of absolute values and a preset threshold value. A program used in an image processing apparatus that synchronizes Bayer images input from a single-plate image sensor by interpolation and outputs them as color images,
Calculating a temporary interpolation value corresponding to the RGB color image data of the pixel of interest from the RGB color image data of the surrounding pixels;
Calculating hue and saturation from the calculated RGB color image data of the pixel of interest;
The calculated hue of the target pixel and its surrounding pixels are compared, and when the difference in hue between the target pixel and its peripheral pixels exceeds a predetermined value, it is determined that the target pixel is a false color And steps to
Replacing the hue of the pixel of interest with a dominant hue around it when it is determined that the false color; and
Calculating a filter coefficient based on the hue of the replaced target pixel or the calculated hue of the target pixel and the hue information of the surrounding pixels;
And a step of outputting RGB color image data based on the calculated filter coefficients. The step of calculating the hue and saturation includes
Comparing the RGB color image data of the pixel of interest with each other and labeling with Max, Mid, and Min;
Calculating the hue of the pixel of interest based on the magnitude relationship between the R, G, and B color image data of the pixel of interest and the magnitude relationship of the difference value between Max and Min and the difference value between Mid and Min. The program according to claim 4, comprising: The step of determining the false color includes
Comparing the saturation of the pixel of interest determined by the step of calculating the hue and saturation with a preset saturation threshold;
Comparing the sum of the absolute values of the differences between the hue of the pixel of interest and the surrounding pixels and a preset threshold;
The program according to claim 4, further comprising a step of determining the presence or absence of a false color from these comparison results.

Images