JP7106470B2 - Image processing method, image processing apparatus - Google Patents

Description

Article 30, Paragraph 2 of the Patent Law applies Product release date: March 9, 2018 Product name: Mirrorless single-lens OLYMPUS PEN E-PL9

The present invention relates to an image processing method and an image processing apparatus for changing a luminance signal of an image composed of a plurality of pixels.

For example, when the luminance signal Y is generated from the sensor RGB, it differs from the luminance signal generated by the sRGB of the display system, and the difference increases as the saturation increases. In particular, in a color region such as blue or magenta, the luminance value of the luminance signal Y generated by the sensor RGB is too high compared to the luminance signal generated by the sRGB of the display system.

Therefore, Japanese Patent Application Laid-Open No. 2015-041856 describes a technique for adjusting the luminance signal of pixels constituting a color image according to the saturation or hue of the pixel. is in the process of being lowered. Since the technique described in the publication is a technique for correcting luminance signals created in different color spaces, for example, the gain is set so that the luminance signal does not become discontinuous at the hue boundary, and the hue signal changes slightly. However, the luminance signal does not change significantly.

By the way, it is known that human vision is sensitive to even a slight change in luminance, although it is difficult to perceive a slight change in hue or saturation. Utilizing these human visual characteristics, for example, in JPEG, etc., the information amount of color difference signals responsible for hue, such as YCC 4:2:2, is thinned out.

JP 2015-041856 A

By the way, in order to aim for a specific visual effect, the luminance signal may be changed relatively greatly according to the hue signal. For example, the luminance signal value of only the pixel signal of the hue corresponding to the human skin color is reduced relatively steeply.

With such a technique, even a slight change in the hue signal causes a relatively large change in the luminance signal in a range in which the amount of change in the luminance signal relative to the amount of change in the hue signal is large. As described above, the human visual system does not perceive slight changes in hue, but is sensitive to changes in luminance. Even though it is an area, a luminance step occurs and it looks like a striped pattern of luminance unevenness, which may give a sense of discomfort.

SUMMARY OF THE INVENTION The present invention has been made in view of the above circumstances. The object is to provide a method and an image processing apparatus.

An image processing method according to an aspect of the present invention includes a first step of superimposing random noise on a hue signal of a certain pixel in an image composed of a plurality of pixels; and a second step of changing the luminance signal of the pixel by the amount of change , wherein the first step changes the hue signal based on the hue signal before the random noise is superimposed. A step of obtaining a slope k related to the amount of change of the luminance signal with respect to the quantity, and superimposing the random noise having a standard deviation corresponding to the absolute value |k| of the slope onto the hue signal .

An image processing apparatus according to another aspect of the present invention includes a noise superimposing unit that superimposes random noise on a hue signal of a certain pixel in an image composed of a plurality of pixels; and a luminance correction unit that changes the luminance signal of the pixel by the amount of change, and the noise superimposing unit adjusts the amount of change in the hue signal based on the hue signal before the random noise is superimposed. A gradient k related to the amount of change in the luminance signal is obtained, and the random noise having a standard deviation corresponding to the absolute value |k| of the gradient is superimposed on the hue signal .

According to the image processing method and the image processing apparatus of the present invention, when the luminance signal is changed according to the hue signal, it is possible to reduce the conspicuous luminance unevenness caused by the luminance step caused by a slight difference in the hue signal. can.

1 is a block diagram showing the configuration of an image processing apparatus according to Embodiment 1 of the present invention; FIG. FIG. 4 is a diagram showing an example of distribution of hue signals of pixels in an area with an image according to the first embodiment; FIG. 4 is a diagram showing an example in which the luminance signal is changed according to the hue signal of each pixel in an area with an image without superimposing random noise in the first embodiment; FIG. 10 is a diagram showing an example of changing the luminance signal according to the hue signal of each pixel superimposed with random noise after superimposing random noise on the hue signal of each pixel in a certain region of the image in the first embodiment. FIG. 2 is a graph showing an example of a luminance signal that changes according to a luminance correction value corresponding to a hue signal converted into a luminance gain in the first embodiment; FIG. 4 is a diagram showing an example of changing the standard deviation of random noise according to the absolute value |k| 4 is a flowchart showing processing of the image processing apparatus according to the first embodiment; FIG. 2 is a block diagram showing the configuration of an image processing apparatus according to Embodiment 2 of the present invention; 9 is a flowchart showing processing of the image processing apparatus in the second embodiment; FIG. 3 is a block diagram showing the configuration of an image processing apparatus according to Embodiment 3 of the present invention; 13 is a flowchart showing processing of the image processing apparatus in the third embodiment;

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, embodiments of the present invention will be described with reference to the drawings.
[Embodiment 1]

1 to 7 show Embodiment 1 of the present invention, and FIG. 1 is a block diagram showing the configuration of an image processing apparatus.

First, examples of apparatuses to which the image processing apparatus of the present embodiment is preferably applied include digital cameras, digital video cameras, mobile telephones with imaging functions, computers executing image processing software, and the like.

The image processing apparatus shown in FIG. , and an RGB conversion unit 8 .

An image is composed of a plurality of pixels, and particularly in the image input to the luminance/color difference conversion section 1 in this embodiment, the pixel signal of each pixel is an RGB signal (R signal, G signal, and B signal). (However, the input signals may be other signals, such as complementary color signals, or signals in other color spaces). Therefore, for example, in the case of an image acquired by an imaging device equipped with a primary color Bayer color filter, it is assumed that demosaic processing has been performed before being input to the luminance/color difference conversion unit 1 .

The luminance/color difference converter 1 separates the input pixel signal into a luminance signal and a color difference signal. Specifically, the luminance/color difference conversion unit 1 converts the input RGB signals from the RGB color space to, for example, the YCbCr color space, thereby converting the Y signal as the luminance signal and the Cb and Cr signals as the color difference signals. , will be output.

The hue calculation unit 2 receives the color difference signals Cb and Cr from the luminance/color difference conversion unit 1, and calculates and outputs a hue signal H based on the color difference signals Cb and Cr. Therefore, the luminance/color difference conversion unit 1 and the hue calculation unit 2 separate the pixel signal into the luminance signal Y and the hue signal H. FIG.

The random noise generator 3 generates random noise N to be added to the hue signal H and outputs it. Note that the random noise generation unit 3 is not limited to generating a random number each time to generate the random noise N, but may generate the random noise N by reading a random number table prepared in advance. I do not care.

Here, the random noise generator 3 may generate only random noise N with a constant standard deviation, but random noise with a large standard deviation is generated in a portion where the amount of change in the luminance signal with respect to the amount of change in the hue signal is large. N may be generated, and random noise N with a small standard deviation may be generated in a portion where the amount of change in the luminance signal relative to the amount of change in the hue signal is small.

For example, the random noise generation unit 3 refers to the correction table 5 based on the hue signal H before the random noise is superimposed, and obtains the slope k related to the amount of change in the luminance signal Y with respect to the amount of change in the hue signal H. , random noise having a standard deviation corresponding to the absolute value |k| of the slope is superimposed on the hue signal.

Here, when the luminance correction value of the luminance signal Y in the hue signal H is described as ΔY(H), the slope k is the difference between ΔY(H) and the hue signal (H+ΔH) obtained by making the hue signal H different by ΔH. For example, it is calculated as follows based on the luminance correction value ΔY(H+ΔH).
k = {ΔY(H+ΔH)−ΔY(H)}/{(H+ΔH)−(H)}
= {ΔY(H+ΔH)−ΔY(H)}/ΔH

This slope k may be stored in advance in the correction table 5 according to the hue signal, and may be read out by the random noise generator 3 based on the hue signal (H+ΔH). Alternatively, the random noise generator 3 may read out the luminance correction values ΔY(H) and ΔY(H+ΔH) from the correction table 5 and calculate them using the above-described calculation formula.

Further, when storing polygonal line data as will be described later in the correction table 5, the inclination of the polygonal line data in the hue signal H may be used as k.

FIG. 6 is a diagram showing an example of changing the standard deviation σN of the random noise N according to the absolute value |k|

In the example shown in FIG. 6, σN=0 when |k|<Th (where Th is a predetermined threshold), and σN=σ0 when |k|=Th (where σ0 is a predetermined standard deviation initial value), and when |k|>Th, σN monotonically increases with |k|. Here, it is assumed that the average value of the random noise N is 0, and that the random noise N can take any positive or negative value.

In this case, random noise N is not generated when |k|<Th, and random noise N is generated only when |k|≧Th. Therefore, random noise is superimposed on the hue signal with the slope absolute value |k| will not be superimposed. By performing such processing, it is possible to prevent error diffusion from being wastefully performed on an image portion in which the luminance signal hardly changes even if the hue signal changes, and suppress the occurrence of noise. can.

Here, the predetermined standard deviation initial value σ0 is set to a value larger than 0, but when σ0=0 and the absolute value |k| of the slope reaches the predetermined threshold value Th, |k| Random noise with a gradually increasing standard deviation may be superimposed.

Also, here, the predetermined threshold value Th is set to a value greater than 0, but with Th=0, as the absolute value of the slope |k| It doesn't matter if you do it. In this case, random noise is basically superimposed on all pixel signals of the image, except for pixels where |k|=0.

The adder 4 receives the hue signal H from the hue calculator 2 and the random noise N from the random noise generator 3 . Then, the adder 4 adds the random noise N to the hue signal H and outputs the hue signal (H+N) superimposed with the random noise. Therefore, the adder 4 directly superimposes random noise on the hue signal (an example of indirectly superimposing random noise on the hue signal will be described in another embodiment below). In this way, the random noise generating section 3 and the adding section 4 constitute a noise superimposing section that superimposes random noise on the hue signal of a certain pixel in an image composed of a plurality of pixels.

The correction table 5 is a lookup table (or polygonal line data or the like may be used) stored in a nonvolatile recording medium. The lookup table (or polygonal line data, etc.) records information that provides a correspondence relationship between the hue signal and the luminance correction value ΔY.

An example of information stored in the correction table 5 will be described with reference to FIG. Here, FIG. 5 is a graph showing an example of the luminance signal Y, which changes according to the luminance correction value ΔY corresponding to the hue signal H, converted into luminance gain.

FIG. 5 shows an example in which the luminance signal value is sharply lowered in a certain specific hue range, and the luminance signal value is lowered to about half, for example. The absolute value |k| of the slope of the change amount of the luminance signal Y with respect to the change amount of the hue signal H is equal to or greater than the predetermined threshold value Th in the hue range indicated by the double-headed arrow in FIG.

A luminance correction value calculation unit 6 receives the hue signal (H+N) superimposed with random noise from the addition unit 4, refers to the correction table 5 based on (H+N), and performs luminance correction for changing the luminance signal Y. Get the value ΔY. Therefore, the luminance correction value ΔY, which is the amount of change in the luminance signal Y of the pixel, is the amount of change corresponding to the hue signal (H+N) on which the random noise is superimposed.

If the value of (H+N) does not match the reference value of the correction table 5, the luminance correction value ΔY can be calculated by performing interpolation based on a plurality of reference values sandwiching the value of (H+N). Further, as described above, the brightness correction value ΔY may be calculated from the value of (H+N) using polygonal line data.

The adder 7 receives the luminance signal Y from the luminance/color difference converter 1 and the luminance correction value ΔY from the luminance correction value calculator 6 . Then, the adder 7 adds the luminance correction value ΔY to the luminance signal Y to output the corrected luminance signal (Y+ΔY). In this way, the correction table 5, the luminance correction value calculator 6, and the adder 7 constitute a luminance correction unit that changes the luminance signal of the pixel by the amount of change according to the hue signal (H+N) on which the random noise is superimposed. ing.

Instead of storing the luminance correction value ΔY corresponding to the hue signal in the correction table 5, the luminance gain corresponding to the hue signal as shown in FIG. 5 may be stored in the correction table 5. . In this case, the luminance gain gy is obtained based on the hue signal (H+N), and the luminance signal Y is multiplied by the luminance gain gy in the multiplication section instead of the addition section 7 . Processing by such a configuration corresponds to setting the luminance correction value ΔY to (gy−1)Y in the configuration shown in FIG. Furthermore, in this case, by changing the definition of the slope k from the above, for example,
k = {gy (H + ΔH) - gy (H)} / ΔH
I don't mind.

The RGB converter 8 receives the corrected luminance signal (Y+ΔY) from the adder 7 and the color difference signals Cb and Cr from the luminance/color difference converter 1 . The RGB converter 8 converts the corrected luminance signal (Y+ΔY) and color difference signals Cb and Cr into RGB signals and outputs them. Here, since the RGB signals output from the RGB conversion unit 8 have different signal values from the RGB signals (R signal, G signal, B signal) input to the luminance/color difference conversion unit 1, the prime "'" is omitted. , R' signal, G' signal, and B' signal.

Next, FIG. 7 is a flow chart showing processing of the image processing apparatus.

When this process is entered from the main process (not shown) in the image processing apparatus, the luminance/color difference converter 1 receives a pixel signal (RGB signal) of one pixel (step S1).

Then, the luminance/color difference converter 1 separates the input RGB signals into a luminance signal Y and color difference signals Cb and Cr (step S2).

Subsequently, the hue calculator 2 calculates the hue signal H based on the color difference signals Cb and Cr (step S3).

The random noise generator 3 receives the hue signal H, refers to the correction table 5, and obtains the gradient k of the hue signal H. FIG. Then, the random noise generator 3 generates random noise N with a standard deviation σN corresponding to the absolute value |k| of the slope (step S4).

The adder 4 adds the random noise N to the hue signal H and outputs the hue signal (H+N) superimposed with the random noise (step S5).

The brightness correction value calculator 6 refers to the correction table 5 based on the hue signal (H+N) on which the random noise is superimposed, and obtains the brightness correction value ΔY (step S6).

The adder 7 corrects the luminance signal by adding the luminance correction value ΔY to the luminance signal Y (step S7).

The RGB conversion unit 8 converts the corrected luminance signal (Y+ΔY) and the color difference signals Cb and Cr input from the luminance/color difference conversion unit 1 from the YCbCr color space to the RGB color space, thereby converting the R′G′ A B' signal is generated (step S8).

After that, it is determined whether or not the processing for all the pixels in the image has been completed (step S9). such processing.

On the other hand, if it is determined in step S9 that the processing for all pixels has been completed, the processing returns from this processing to the main processing and the like.

Next, how an image changes as a result of such processing will be described with reference to FIGS. 2 to 4. FIG.

First, FIG. 2 is a diagram showing a distribution example of hue signals of each pixel in a certain area of an image.

In FIG. 2, in an image (original image) before correction to change the luminance signal, for example, in an area with 8×8 pixels, the hue signal on the upper left side is H and the hue signal on the lower right side is (H+Δh). example. Therefore, the lower right side of the region differs in hue signal from the upper left side by Δh.

Here, it is assumed that the hue signal H and the hue signal (H+Δh) appear to have substantially similar hues to the human eye, and that there is not much difference between them.

In addition, hatching in FIGS. 2 to 4 indicates the state of the luminance signal. In FIG. 2, the luminance signal Y is the same in the hue signal H region and the hue signal (H+Δh) region (for simplicity, Furthermore, the saturation signal is also the same). In this case, the entire 8×8 pixel area shown in FIG. 2 appears to humans as a flat area of substantially uniform color.

FIG. 3 is a diagram showing an example in which the luminance signal is changed according to the hue signal of each pixel in a certain region of the image without superimposing random noise.

Different hues result in different luminance correction values ΔY. That is, a luminance correction value (ΔY1) obtained by referring to the correction table 5 based on the hue signal H and a luminance correction value (ΔY2) obtained by referring to the correction table 5 based on the hue signal (H+Δh). different from

Therefore, after the luminance correction, the luminance signal of the pixel with the hue signal H is (Y+ΔY1) and the luminance signal of the pixel with the hue signal (H+Δh) is (Y+ΔY2), and the signal values are different.

As described above, the human eye can hardly perceive a slight change in hue or saturation (Δh described above), but a slight change in luminance (the luminance correction value is sensitive to the difference |ΔY1−ΔY2|). Therefore, in the 8×8 pixel area, after the luminance correction, the difference in luminance may be perceived clearly as indicated by the difference in hatching in FIG.

In this case, the area that appears to be almost flat in the original image of FIG. It is recognized as unevenness.

On the other hand, in FIG. 4, after superimposing the random noise N on the hue signal of each pixel in a certain area of the image, the luminance signal is changed according to the hue signal of each pixel superimposed with the random noise N. FIG. 4 is a diagram showing an example; The random noise N in FIG. 4 uses the same symbol for each pixel, but is a different random value for each pixel.

As described above, in this embodiment, since the luminance correction value ΔY is calculated based on the hue signal (H+N) superimposed with random noise, the luminance signal differs for each pixel. As a result, in FIG. 3, a relatively low luminance signal portion and a relatively high luminance signal portion were divided into upper left and lower right portions, whereas in FIG. , and the brightness is error-diffused to obscure the brightness step.

Observing such an image, one feels that the difference in color between the upper left side and the lower right side changes more smoothly. be able to.

According to the first embodiment, the random noise N is superimposed on the hue signal of the pixel, and the luminance signal of the pixel is changed by the amount of change corresponding to the hue signal on which the random noise N is superimposed. It is possible to reduce conspicuous luminance unevenness caused by a luminance step caused by a slight difference in hue signals.

Further, since random noise N having a standard deviation corresponding to the absolute value |k| random noise N can be superimposed.

Further, since the random noise N is not superimposed on the hue signal whose absolute value |k| of the slope is less than the predetermined threshold, It is possible to prevent the error diffusion from being performed in vain and suppress the occurrence of noise.

Then, the pixel signal is separated into a luminance signal and a hue signal, random noise N is superimposed on the separated hue signal, and the separated luminance signal is changed. can be processed without

In addition, since the hue signal is calculated based on the color difference signal separated from the pixel signal, it is possible to convert the pixel in the original signal format simply by performing color space conversion processing on the corrected luminance signal and color difference signal. You can easily switch back to the signal.

In particular, by separating the RGB signal into the luminance signal Y and the color difference signals Cb and Cr, general-purpose color space conversion processing from the RGB color space to the YCbCr color space can be used.

Further, the addition unit 4 directly superimposes the random noise N generated by the random noise generation unit 3 on the hue signal calculated by the hue calculation unit 2, thereby changing the hue without impairing the randomness. can be done.
[Embodiment 2]

8 and 9 show Embodiment 2 of the present invention, and FIG. 8 is a block diagram showing the configuration of an image processing apparatus.

In the second embodiment, the same reference numerals are assigned to the same parts as in the first embodiment, and the description thereof will be omitted as appropriate, and only the different points will be mainly described.

In the image processing apparatus of this embodiment, the random noise generator 3 is connected to the luminance/color difference converter 1 and the correction table 5 . The random noise generator 3 receives the color difference signals Cb and Cr from the luminance/color difference converter 1, calculates the hue signal H based on the color difference signals Cb and Cr, and refers to the correction table 5 based on the calculated hue signal H. to obtain the slope k. Then, the random noise generation unit 3 generates random noise N1 having a standard deviation σN(Cb) for the color difference signal Cb according to the absolute value |k| Generate random noise N2 with standard deviation σN(Cr) for signal Cr.

Note that the standard deviation σN(Cb) and the standard deviation σN(Cr) may be different values or the same value, but the random noise N1 and the random noise N2 are assumed to be different values.

The reason why the random noise N1 for the color difference signal Cb and the random noise N2 for the color difference signal Cr are different is as follows. In other words, it is assumed that a certain pixel signal has a hue of, for example, an oblique direction of 45° on the CrCb plane. At this time, if the random noise added to the color difference signal Cb and the random noise added to the color difference signal Cr are set to the same value, the hue signal does not change even if the saturation signal changes. This is because ΔY cannot be changed randomly.

Furthermore, in this embodiment, the adder 4 is arranged before the hue calculator 2 and connected to the luminance/color difference converter 1 , the random noise generator 3 and the hue calculator 2 . The adder 4 inputs the color difference signals Cb and Cr from the luminance/color difference converter 1 and inputs the random noises N1 and N2 from the random noise generator 3 . Then, the adder 4 superimposes the random noise N1 on the color difference signal Cb, superimposes the random noise N2 on the color difference signal Cr, and converts the color difference signals (Cb+N1) and (Cr+N2) superposed with the random noise to the hue calculation unit 2. Output to

The hue calculation unit 2 receives the color difference signals (Cb+N1) and (Cr+N2) superimposed with random noise from the luminance/color difference conversion unit 1, calculates and outputs the hue signal (H+N) superimposed with random noise.

That is, while the random noise N is directly superimposed on the hue signal H in the first embodiment described above, in the present embodiment, by superimposing the random noises N1 and N2 on the color difference signals Cb and Cr, Random noise N is indirectly superimposed on the hue signal H.

Other configurations and processes of the luminance/color difference converter 1, the correction table 5, the luminance correction value calculator 6, the adder 7, and the RGB converter 8 are the same as those of the first embodiment.

Next, FIG. 9 is a flow chart showing processing of the image processing apparatus.

When this process is entered and the processes of steps S1 and S2 described above are performed, the random noise generator 3 inputs the color difference signals Cb and Cr to calculate the hue signal H, and generates the correction table 5 based on the hue signal H. The slope k in the hue signal H is obtained by referring to the above. Then, the random noise generator 3 generates random noise N1 having a standard deviation σN(Cb) corresponding to the absolute value |k| of the slope, and a standard deviation σN(Cr) corresponding to the absolute value |k| of random noise N2 is generated (step S4A).

Subsequently, the adder 4 superimposes the random noise N1 on the color difference signal Cb, superimposes the random noise N2 on the color difference signal Cr, and outputs the color difference signals (Cb+N1) and (Cr+N2) superimposed with the random noise ( step S5A).

Further, the hue calculator 2 calculates a hue signal (H+N) superimposed with random noise based on the color difference signals (Cb+N1) and (Cr+N2) superimposed with random noise (step S3A).

After that, the processes of steps S6 to S9 are performed as described above, and if it is determined in step S9 that the processes for all pixels have been completed, the process returns.

According to the second embodiment, substantially the same effects as those of the above-described first embodiment are obtained, and also by superimposing the random noises N1 and N2 on the color difference signals, the random noise N is indirectly superimposed on the hue signal. can do.
[Embodiment 3]

10 and 11 show Embodiment 3 of the present invention, and FIG. 10 is a block diagram showing the configuration of an image processing apparatus.

In the third embodiment, the same reference numerals are assigned to the same parts as in the above-described first and second embodiments, and the description thereof will be omitted as appropriate, and only the different points will be mainly described.

In the image processing apparatus of the second embodiment shown in FIG. 8, the Cb line and the Cr line output from the luminance/color difference conversion unit 1 are directly connected to the RGB conversion unit 8, but in the image processing apparatus of this embodiment , and the adder 4 . Accordingly, color difference signals (Cb+N1) and (Cr+N2) on which random noise is superimposed are input from the adder 4 to the RGB converter 8 .

The RGB conversion unit 8 receives the corrected luminance signal (Y+ΔY) from the addition unit 7 and the color difference signals (Cb+N1) and (Cr+N2) superimposed with random noise from the addition unit 4 . The RGB converter 8 converts the corrected luminance signal (Y+ΔY) and color difference signals (Cb+N1) and (Cr+N2) into RGB signals and outputs them. Here, the RGB signal output from the RGB conversion unit 8 not only differs in signal value from the RGB signal (R signal, G signal, B signal) input to the luminance/color difference conversion unit 1, but also differs from the above-described second embodiment. Since the R' signal, the G' signal and the B' signal output from the RGB converter 8 have different signal values, a double prime "" is attached to the R" signal, the G" signal and the B" signal. signal.

When such processing is performed, the color difference signals change from Cb and Cr to (Cb+N1) and (Cr+N2), but as described above, human vision is insensitive to changes in hue or saturation. Therefore, even if the color difference signal, which mainly carries information on hue and saturation, changes to some extent, it does not cause much discomfort. Also, by adding some color noise, it is possible to aim for a visual effect of graininess similar to images shot with film.

Other configurations and processes of the luminance/color difference conversion unit 1, the hue calculation unit 2, the random noise generation unit 3, the correction table 5, the luminance correction value calculation unit 6, and the addition unit 7 are the same as those of the second embodiment described above. .

FIG. 11 is a flow chart showing processing of the image processing apparatus.

When the processing of steps S1, S2, S4A, S5A, S3A, S6, and S7 described above is performed after entering this processing, the RGB conversion unit 8 converts the corrected luminance signal (Y + ΔY) and the random noise superimposed By converting the color difference signals (Cb+N1) and (Cr+N2) from the YCbCr color space to the RGB color space, R″ signal, G″ signal and B″ signal are generated (step S8B).

Thereafter, the process of step S9 is performed as described above, and if it is determined in step S9 that the process for all pixels has been completed, the process returns.

According to the third embodiment, substantially the same effects as those of the first and second embodiments can be obtained, and a grainy visual effect similar to that of an image photographed with a film can be aimed at.

The processing of each unit described above may be performed by one or more processors configured as hardware. For example, each unit may be a processor configured as an electronic circuit, or may be each circuit unit in a processor configured with an integrated circuit such as an FPGA (Field Programmable Gate Array). Alternatively, a processor composed of one or more CPUs may read and execute a computer program recorded on a recording medium, thereby executing the function of each unit.

In addition, although the image processing apparatus has been mainly described above, a control method for controlling the image processing apparatus as described above may be used. It may be a computer-readable, non-transitory recording medium for recording a computer program.

Furthermore, the present invention is not limited to the above-described embodiment as it is, and can be embodied by modifying the constituent elements without departing from the scope of the present invention at the implementation stage. Moreover, various aspects of the invention can be formed by appropriate combinations of the plurality of constituent elements disclosed in the above embodiments. For example, some components may be omitted from all components shown in the embodiments. Furthermore, components across different embodiments may be combined as appropriate. As described above, it goes without saying that various modifications and applications are possible without departing from the gist of the invention.

DESCRIPTION OF SYMBOLS 1... Luminance-color difference conversion part 2... Hue calculation part 3... Random noise generation part 4... Addition part 5... Correction table 6... Luminance correction value calculation part 7... Addition part 8... RGB conversion part

Claims (7)

A first step of superimposing random noise on a hue signal of a pixel in an image composed of a plurality of pixels;
a second step of changing the luminance signal of the pixel by a change amount according to the hue signal on which the random noise is superimposed;
including
In the first step, based on the hue signal before the random noise is superimposed, a slope k relating to the amount of change in the luminance signal with respect to the amount of change in the hue signal is obtained, and the absolute value of the slope | An image processing method , comprising: superimposing the random noise having a standard deviation corresponding to k| on the hue signal . The first step superimposes the random noise on the hue signal whose slope absolute value |k| is equal to or greater than a predetermined threshold, and superimposes the random noise on the hue signal whose slope absolute value |k| is a step of not superimposing the random noise. further comprising a third step of separating the pixel signal into the luminance signal and the hue signal;
The first step is a step of superimposing the random noise on the hue signal separated by the third step,
2. The image processing method according to claim 1, wherein said second step is a step of changing said luminance signal separated by said third step. The third step is
a fourth step of separating the pixel signal into the luminance signal and the color difference signal;
a fifth step of calculating the hue signal based on the color difference signal;
4. The image processing method according to claim 3, comprising: The first step directly superimposes the random noise on the hue signal, or superimposes the random noise on the color difference signal, so that the random noise is added to the hue signal calculated in the fifth step. 5. The image processing method according to claim 4, wherein the step of indirectly superimposing . The fourth step is a step of converting the pixel signals from an RGB color space to a YCbCr color space, wherein the pixel signals before being separated by the fourth step are RGB signals; 5. The image processing method according to claim 4, wherein the luminance signal separated in the fourth step is the Y signal, and the color difference signals are the Cb signal and the Cr signal. A noise superimposition unit that superimposes random noise on a hue signal of a certain pixel in an image composed of a plurality of pixels;
a luminance correction unit that changes the luminance signal of the pixel by a change amount according to the hue signal on which the random noise is superimposed;
and
The noise superimposing unit acquires a slope k related to the amount of change in the luminance signal with respect to the amount of change in the hue signal based on the hue signal before the random noise is superimposed thereon, and obtains the absolute value |k of the slope. An image processing apparatus, wherein the random noise having a standard deviation corresponding to | is superimposed on the hue signal.

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