CN110400260B - Image processing method and device - Google Patents
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Abstract
The invention provides an image processing method and device, which are characterized in that flat area expansion and self-adaptive flat area pixel detection are added into a CLAHE algorithm, and the amplitude of a brightness histogram of a rectangular area where flat area pixels are located is modified to be a specific value, so that the contrast of the pixels with the specific amplitude after histogram equalization processing is kept unchanged, the local contrast and brightness of an image can be adaptively enhanced, noise amplification can be well inhibited, and the problems of color gradation, arc line and the like of the image can be avoided.
Description
Technical Field
The present invention relates to the field of image processing technologies, and in particular, to an image processing method and apparatus.
Background
In the use process of the mobile phone, a user often needs to watch the mobile phone screen under strong ambient light, for example, strong sunlight outdoors or strong light indoors, and due to the strong ambient light, the amount of ambient light reflected by the mobile phone screen is large, so that the visibility of the content displayed on the mobile phone screen observed by the user is reduced. At this time, users often adjust the backlight brightness of the mobile phone screen to be high, which can be alleviated in many cases, but some mobile phone screens have a low maximum brightness value, even if the backlight brightness is increased to the maximum value, the images still remain invisible, and the increase of the backlight brightness of the screen can significantly increase the power consumption of the mobile phone, and reduce the standby time. And by a real-time image processing method of Local Tone Mapping (LTM), the image pixel value is adaptively adjusted according to the ambient light intensity, so that the Local contrast and brightness of the image can be improved, the visibility of the image on a mobile phone screen is enhanced, and the problems of insufficient screen backlight and large power consumption are solved, which is a sunlight readable function of the mobile phone. Among various real-time local tone mapping algorithms, the Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm has the advantages of good effect, strong universality and low hardware cost, is the most common real-time local tone mapping algorithm, and has the principle that an image is divided into a plurality of rectangular small blocks, Histogram Equalization is respectively carried out on each rectangular small block to realize local Contrast improvement, and finally, the processing result of each block is smoothed through interpolation to reduce the calculation complexity.
In the process of implementing the invention, the inventor finds that at least the following technical problems exist in the prior art:
the most important feature of the CLAHE algorithm is that the contrast amplification amplitude is limited for each small rectangular area to overcome the problem of over-amplification of noise, but because the image displayed on the screen of the mobile phone has complex sources and different quality, in order to be suitable for images with various noise degrees, the contrast enhancement amplitude is generally not set to be very high, so as to avoid the problems of noise amplification, color gradation and arc of the image and the like while enhancing the contrast and brightness of the image. In practical use, when the ambient light is very strong, the local contrast and brightness of the image must be relatively improved to a great extent, so as to meet the requirement of the user on the improvement of the visibility of the screen. It follows that it is difficult for the CLAHE algorithm to balance the relationship between contrast amplification and noise suppression, for example, to improve image contrast and brightness to a level satisfactory to the user, but at the same time problems such as noticeable noise, tone scale and camber may occur.
Disclosure of Invention
The image processing method and the image processing device can adaptively enhance the local contrast and brightness of the image, simultaneously inhibit noise amplification and avoid the problems of color gradation, arc line and the like of the image.
In a first aspect, the present invention provides an image processing method, including:
calculating the pixel gradient value of each pixel in the brightness component of the image to be processed, counting a gradient histogram corresponding to the pixel with the pixel gradient value smaller than a first threshold value T1, and counting the noise level T2 of the brightness component of the image to be processed according to the gradient histogram;
dividing the brightness component of the image to be processed into a plurality of rectangular areas, counting a first brightness histogram of each rectangular area, and determining a first flat area composed of pixels with pixel gradient values smaller than the noise level T2 in each rectangular area;
performing flat area expansion processing on each first flat area by using a low-pass filter to obtain a corresponding second flat area, counting a brightness histogram of each second flat area, calculating an amplitude ratio of the brightness histogram of the second flat area to the brightness histogram of a rectangular area where the second flat area is located aiming at each brightness value, determining a corresponding brightness value larger than the amplitude ratio of a third threshold value T3, and setting pixels with the corresponding brightness value in the rectangular area where the second flat area is located as flat area pixels;
calculating the minimum effective amplitude H1 of the first brightness histogram of each rectangular area, uniformly modifying the amplitude of the brightness histogram of the rectangular area where the flat area pixels are located into H1, accumulating and summing the original amplitude value before modification and the difference value of H1, and adding the sum to the peak position of the first brightness histogram of the rectangular area where the flat area pixels are located to obtain a second brightness histogram of each rectangle;
And performing self-adaptive equalization processing on the second brightness histogram of each rectangle by using a CLAHE algorithm to obtain a final brightness value of each pixel in the brightness component of the image to be processed.
In a second aspect, the present invention provides an image processing apparatus comprising:
the noise level statistical module is used for calculating the pixel gradient value of each pixel in the brightness component of the image to be processed, counting a gradient histogram corresponding to the pixel with the pixel gradient value smaller than a first threshold value T1, and counting the noise level T2 of the brightness component of the image to be processed according to the gradient histogram;
the first brightness histogram calculation module is used for dividing the brightness component of the image to be processed into a plurality of rectangular areas and counting a first brightness histogram of each rectangular area;
a first flat region determination module for determining a first flat region composed of pixels of a pixel gradient value smaller than the noise level T2 in each rectangular region;
the second flat area and the brightness histogram determination module thereof are used for carrying out flat area expansion processing on each first flat area by adopting a low-pass filter to obtain a corresponding second flat area and counting the brightness histogram of each second flat area;
A flat area pixel determination module, configured to calculate, for each luminance value, an amplitude ratio between a luminance histogram of the second flat area and a luminance histogram of a rectangular area where the second flat area is located, determine a corresponding luminance value that is greater than the amplitude ratio of a third threshold T3, and set, as a flat area pixel, a pixel in the rectangular area where the second flat area is located and having the corresponding luminance value;
the amplitude calculation module is used for calculating the lowest effective amplitude H1 of the first brightness histogram of each rectangular area;
the second brightness histogram calculation module is used for uniformly modifying the amplitude of the brightness histogram of the rectangular area where the flat area pixels are located into H1, accumulating and summing the original amplitude value before modification and the difference value of H1, and then adding the sum to the peak position of the first brightness histogram of the rectangular area where the flat area pixels are located to obtain a second brightness histogram of each rectangle;
and the self-adaptive equalization module is used for performing self-adaptive equalization processing on the second brightness histogram of each rectangle by adopting a CLAHE algorithm so as to obtain the final brightness value of each pixel in the brightness component of the image to be processed.
Compared with the prior art, the image processing method and the image processing device provided by the embodiment of the invention have the advantages that the flat area expansion and the self-adaptive flat area pixel detection are added into the CLAHE algorithm, and the amplitude of the luminance histogram of the rectangular area where the flat area pixel is located is modified to be a specific value, so that the contrast of the pixel with the amplitude of the specific value after histogram equalization processing is kept unchanged, the local contrast and the luminance of the image can be adaptively enhanced, the noise amplification can be well inhibited, and the problems of color gradation, arc line and the like of the image can be avoided. In addition, due to the self-adaptive noise suppression effect, the method is suitable for application scenes with strong contrast improvement, low in implementation complexity and suitable for hardware implementation and mobile phone real-time display processing.
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FIG. 1 is a flowchart of an image processing method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an image processing apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The present invention provides an image processing method, as shown in fig. 1, the method comprising:
s11, calculating the pixel gradient value of each pixel in the brightness component of the image to be processed, counting a gradient histogram corresponding to the pixel with the pixel gradient value smaller than a first threshold value T1, and counting the noise level T2 of the brightness component of the image to be processed according to the gradient histogram.
S12, dividing the brightness component of the image to be processed into a plurality of rectangular areas, counting a first brightness histogram of each rectangular area, and determining a first flat area composed of pixels with pixel gradient values smaller than the noise level T2 in each rectangular area.
S13, performing flat area expansion processing on each first flat area by adopting a low-pass filter to obtain a corresponding second flat area, counting a brightness histogram of each second flat area, calculating the amplitude ratio of the brightness histogram of the second flat area to the brightness histogram of a rectangular area where the second flat area is located according to each brightness value, determining a corresponding brightness value larger than the amplitude ratio of a third threshold value T3, and setting pixels with the corresponding brightness values in the rectangular area where the second flat area is located as flat area pixels.
The principle of the flat region expanding process is as follows: for example, the luminance range of the flat region is 0 to 255, the luminance range of the flat region after the above step is 64 to 128, but considering the pixels at the edge of the flat region, the number of the pixels belonging to the flat region and the number of the pixels not belonging to the flat region are close to each other, for example, the number of the pixels with the luminance of 64 is 10 counted on the histogram of the luminance of the rectangular region, wherein the number of the pixels belonging to the flat region judged by the above step is 5, the number of the pixels in the non-flat region also is 5, the number of the pixels judged as the flat region is not dominant, and the subsequent judgment method (the ratio is 5/10) may make the 64 pixels with the luminance judged as the pixels in the non-flat region, and affect the effect of the algorithm at the edge of the flat region. To avoid this, the pixel statistics at the peak near the center of the flat area of the luminance histogram can be adaptively reduced by low-pass filtering (e.g. 5 th order FIR low-pass filter), and the reduced pixel statistics can be compensated to the edge pixel statistics, for example, the statistics of luminance 64 on the luminance histogram of the flat area is increased from 5 to 8, and the ratio is 8/10, so that the subsequent judgment method will have a higher probability to judge the luminance 64 pixel as the flat area pixel for processing.
S14, calculating the least effective amplitude H1 of the first brightness histogram of each rectangular area, uniformly modifying the amplitude of the brightness histogram of the rectangular area where the flat area pixels are located into H1, performing cumulative summation on the difference value between the original amplitude value before modification and H1, and adding the sum value to the peak position of the first brightness histogram of the rectangular area where the flat area pixels are located to obtain a second brightness histogram of each rectangle;
s15, performing adaptive equalization processing on the second brightness histogram of each rectangle by using a CLAHE algorithm to obtain a final brightness value of each pixel in the brightness component of the image to be processed.
The subsequent processing is processed according to the CLAHE algorithm flow, except that the original luminance statistical histogram of the CLAHE algorithm is replaced by the second luminance histogram of each rectangle. The process comprises the following steps: for each rectangular area, carrying out contrast amplitude limiting and redistribution operations on the new brightness histogram in sequence to generate a brightness histogram with contrast amplitude limiting; then, histogram equalization processing is carried out, and a brightness mapping (tone mapping) table of each rectangular area is generated; finally, for each pixel, the brightness mapping tables of the adjacent four areas are used for respectively finding out the corresponding four mapping values, and then the final brightness value of each pixel is interpolated by using bilinear interpolation according to the distance from the pixel to the centers of the four rectangular areas.
Compared with the prior art, the image processing method provided by the embodiment of the invention has the advantages that the flat area expansion and the self-adaptive flat area pixel detection are added into the CLAHE algorithm, and the amplitude of the brightness histogram of the rectangular area where the flat area pixel is located is modified to be the specific value, so that the contrast of the pixel with the amplitude of the specific value after the histogram equalization processing is kept unchanged, the local contrast and brightness of the image can be adaptively enhanced, the noise amplification can be well inhibited, the problems of color gradation, arc lines and the like of the image are avoided, in addition, the self-adaptive noise inhibiting effect is realized, the image processing method is suitable for application scenes with strong contrast improvement, the complexity is low, and the image processing method is suitable for hardware realization and mobile phone real-time display processing.
Optionally, the calculating the pixel gradient value of each pixel in the luminance component of the image to be processed includes:
and calculating a horizontal gradient value and a vertical gradient value of each pixel in the brightness component of the image to be processed, and adding the horizontal gradient value and the vertical gradient value to obtain the gradient value of the pixel.
Optionally, the counting the noise level T2 of the luminance component of the image to be processed according to the gradient histogram includes:
If (G) max ×2<T1)，T2＝G max X 2; otherwise, T2 ═ T1;
wherein G is max And the pixel gradient value of each pixel in the brightness component of the image to be processed is obtained.
Optionally, the calculating of the least significant amplitude H1 of the luminance histogram of each rectangular region is implemented by:
H1＝(M×N)/(V max -V min ) And M and N are the length and width of the rectangular region respectively, and Vmax and Vmin are the maximum value and the minimum value of the brightness of the rectangular region respectively.
As shown in fig. 2, an embodiment of the present invention provides an image processing apparatus, including:
the noise level statistical module is used for calculating the pixel gradient value of each pixel in the brightness component of the image to be processed, counting a gradient histogram corresponding to the pixel with the pixel gradient value smaller than a first threshold value T1, and counting the noise level T2 of the brightness component of the image to be processed according to the gradient histogram;
the first brightness histogram calculation module is used for dividing the brightness component of the image to be processed into a plurality of rectangular areas and counting a first brightness histogram of each rectangular area;
a first flat region determination module for determining a first flat region composed of pixels of a pixel gradient value smaller than the noise level T2 in each rectangular region;
The second flat area and the brightness histogram determination module thereof are used for carrying out flat area expansion processing on each first flat area by adopting a low-pass filter to obtain a corresponding second flat area and counting the brightness histogram of each second flat area;
a flat area pixel determination module, configured to calculate, for each luminance value, an amplitude ratio between a luminance histogram of the second flat area and a luminance histogram of a rectangular area where the second flat area is located, determine a corresponding luminance value that is greater than the amplitude ratio of a third threshold T3, and set, as a flat area pixel, a pixel in the rectangular area where the second flat area is located and having the corresponding luminance value;
the amplitude calculation module is used for calculating the least significant amplitude H1 of the first brightness histogram of each rectangular area;
the second brightness histogram calculation module is used for uniformly modifying the amplitude of the brightness histogram of the rectangular area where the flat area pixel is located into H1, accumulating and summing the original amplitude value before modification and the difference value of H1, and adding the sum to the peak position of the first brightness histogram of the rectangular area where the flat area pixel is located to obtain a second brightness histogram of each rectangle;
and the self-adaptive equalization module is used for performing self-adaptive equalization processing on the second brightness histogram of each rectangle by adopting a CLAHE algorithm so as to obtain the final brightness value of each pixel in the brightness component of the image to be processed.
Compared with the prior art, the image processing device provided by the embodiment of the invention has the advantages that the flat area expansion and the self-adaptive flat area pixel detection are added into the CLAHE algorithm, and the amplitude of the brightness histogram of the rectangular area where the flat area pixel is located is modified to be the specific value, so that the contrast of the pixel with the amplitude of the specific value after the histogram equalization processing is kept unchanged, the local contrast and brightness of the image can be adaptively enhanced, the noise amplification can be well inhibited, the problems of color gradation, arc lines and the like of the image are avoided, in addition, the self-adaptive noise inhibiting effect is realized, the image processing device is suitable for application scenes with strong contrast improvement, the complexity is low, and the device is suitable for hardware realization and real-time display processing of a mobile phone.
Optionally, the noise level statistics module includes a gradient pixel value calculation unit, where the gradient pixel value calculation unit is configured to calculate a horizontal gradient value and a vertical gradient value of each pixel in the luminance component of the image to be processed, and add the horizontal gradient value and the vertical gradient value to obtain the pixel gradient value.
Optionally, the noise level statistic module includes a noise level calculation unit, and the noise level calculation unit is configured to obtain the noise level T2 by: if (G) max ×2<T1)，T2＝G max X 2; otherwise, T2 ═ T1; wherein G is max And the pixel gradient value of each pixel in the brightness component of the image to be processed is obtained.
Optionally, the amplitude calculation module obtains the least significant amplitude H1 of the luminance histogram of each rectangular region by:
H1＝(M×N)/(V max -V min ) And M and N are the length and width of the rectangular region respectively, and Vmax and Vmin are the maximum value and the minimum value of the brightness of the rectangular region respectively.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. An image processing method, comprising:
calculating the pixel gradient value of each pixel in the brightness component of the image to be processed, counting a gradient histogram corresponding to the pixel with the pixel gradient value smaller than a first threshold value T1, and counting the noise level T2 of the brightness component of the image to be processed according to the gradient histogram;
dividing the brightness component of the image to be processed into a plurality of rectangular areas, counting a first brightness histogram of each rectangular area, and determining a first flat area composed of pixels with pixel gradient values smaller than the noise level T2 in each rectangular area;
performing flat area expansion processing on each first flat area by using a low-pass filter to obtain a corresponding second flat area, counting a brightness histogram of each second flat area, calculating an amplitude ratio of the brightness histogram of the second flat area to the brightness histogram of a rectangular area where the second flat area is located aiming at each brightness value, determining a corresponding brightness value larger than the amplitude ratio of a third threshold value T3, and setting pixels with the corresponding brightness value in the rectangular area where the second flat area is located as flat area pixels;
calculating the minimum effective amplitude H1 of the first brightness histogram of each rectangular area, uniformly modifying the amplitude of the brightness histogram of the rectangular area where the flat area pixels are located into H1, accumulating and summing the original amplitude value before modification and the difference value of H1, and adding the sum to the peak position of the first brightness histogram of the rectangular area where the flat area pixels are located to obtain a second brightness histogram of each rectangle;
And performing self-adaptive equalization processing on the second brightness histogram of each rectangle by using a CLAHE algorithm to obtain a final brightness value of each pixel in the brightness component of the image to be processed.
2. The method of claim 1, wherein calculating the pixel gradient value for each pixel in the luminance component of the image to be processed comprises:
and calculating a horizontal gradient value and a vertical gradient value of each pixel in the brightness component of the image to be processed, and adding the horizontal gradient value and the vertical gradient value to obtain the gradient value of the pixel.
3. The method according to claim 1, wherein said counting the noise level T2 of the luminance component of the image to be processed according to the gradient histogram comprises:
if (G) max ×2<T1)，T2＝G max X 2; otherwise, T2 ═ T1;
wherein G is max And the pixel gradient value of each pixel in the brightness component of the image to be processed is obtained.
4. The method according to claim 1, wherein the calculating of the least significant amplitude H1 of the luminance histogram of each rectangular region is performed by:
H1＝(M×N)/(V max -V min ) And M and N are the length and width of the rectangular region respectively, and Vmax and Vmin are the maximum value and the minimum value of the brightness of the rectangular region respectively.
5. An image processing apparatus characterized by comprising:
the noise level statistical module is used for calculating the pixel gradient value of each pixel in the brightness component of the image to be processed, counting a gradient histogram corresponding to the pixel with the pixel gradient value smaller than a first threshold value T1, and counting the noise level T2 of the brightness component of the image to be processed according to the gradient histogram;
the first brightness histogram calculation module is used for dividing the brightness component of the image to be processed into a plurality of rectangular areas and counting a first brightness histogram of each rectangular area;
a first flat region determination module for determining a first flat region composed of pixels of a pixel gradient value smaller than the noise level T2 in each rectangular region;
the second flat area and the brightness histogram determination module thereof are used for carrying out flat area expansion processing on each first flat area by adopting a low-pass filter to obtain a corresponding second flat area and counting the brightness histogram of each second flat area;
a flat area pixel determination module, configured to calculate, for each luminance value, an amplitude ratio between a luminance histogram of the second flat area and a luminance histogram of a rectangular area where the second flat area is located, determine a corresponding luminance value that is greater than the amplitude ratio of a third threshold T3, and set, as a flat area pixel, a pixel in the rectangular area where the second flat area is located and having the corresponding luminance value;
The amplitude calculation module is used for calculating the least significant amplitude H1 of the first brightness histogram of each rectangular area;
the second brightness histogram calculation module is used for uniformly modifying the amplitude of the brightness histogram of the rectangular area where the flat area pixels are located into H1, accumulating and summing the original amplitude value before modification and the difference value of H1, and then adding the sum to the peak position of the first brightness histogram of the rectangular area where the flat area pixels are located to obtain a second brightness histogram of each rectangle;
and the self-adaptive equalization module is used for performing self-adaptive equalization processing on the second brightness histogram of each rectangle by adopting a CLAHE algorithm so as to obtain the final brightness value of each pixel in the brightness component of the image to be processed.
6. The apparatus of claim 5, wherein the noise level statistic module comprises a gradient pixel value calculating unit, and the gradient pixel value calculating unit is configured to calculate a horizontal gradient value and a vertical gradient value of each pixel in the luminance component of the image to be processed, and add the horizontal gradient value and the vertical gradient value to obtain the pixel gradient value.
7. The apparatus of claim 5, wherein the noise level statistic module comprises a noise level calculation unit configured to obtain the noise level T2 by: if (G) max ×2<T1)，T2＝G max X 2; otherwise, T2 ═ T1; wherein G is max And the pixel gradient value of each pixel in the brightness component of the image to be processed is obtained.
8. The apparatus of claim 5, wherein the amplitude calculation module obtains the least significant amplitude H1 of the luminance histogram of each rectangular region by:
H1＝(M×N)/(V max -V min ) And M and N are the length and width of the rectangular region respectively, and Vmax and Vmin are the maximum value and the minimum value of the brightness of the rectangular region respectively.
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