CN109118437B - Method and storage medium capable of processing muddy water image in real time - Google Patents
Method and storage medium capable of processing muddy water image in real time Download PDFInfo
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Abstract
The invention provides a method and a storage medium capable of processing a muddy water image in real time, wherein the method comprises the following steps of S1: correcting color cast of the input muddy water image; s2: converting the corrected muddy water image from an RGB space to an LAB space, and acquiring a corresponding brightness component L, a color component A and a color component B; s3: carrying out automatic contrast adjustment and sharpening on the brightness component L in sequence to obtain a processed brightness component L; s4: the processed luminance component L, the color component a and the color component B are combined and converted back to the RGB space. The invention can not only obtain the image quality effects of clear image details, high contrast and true color; the processing process is quick and efficient, and real-time processing and good image effect can be achieved; furthermore, the method also has the advantage of more universal processing effect, and the picture is more suitable for most of human eye watching requirements.
Description
Technical Field
The invention relates to the field of image processing, in particular to a method and a storage medium capable of processing a muddy water image in real time.
Background
In the technical fields of ocean development, underwater engineering, aquaculture and the like, particularly in the application fields of underwater Robots (ROV), ocean equipment, intelligent hardware, underwater movie and television shooting robots, broadcast television equipment, photographic and camera equipment, ocean and underwater observation and monitoring equipment and the like, the underwater high-resolution imaging requirement is often met.
At present, two main underwater imaging modes are available: one is sonar imaging; the second is optical imaging. However, sonar imaging is poor in spatial resolution, and clear image details are difficult to obtain, so that optical imaging becomes a main means of current underwater high-resolution imaging, and has important application value particularly in the aspects of ocean development, underwater engineering, aquaculture and the like. However, underwater images are blurred theoretically, because absorption and scattering of light by water affect the imaging distance of optical imaging and the contrast of an imaging picture, specifically, different absorption rates of water to different wavelength bands of spectra cause severe color shift of the imaging picture, forward scattering causes blurring of image details, and backward scattering causes reduction of image contrast. Especially in inshore and rivers, because the water pollution degree is comparatively serious, impurity such as silt is more in the aquatic, has further reduced optical system's visible distance and imaging definition. Therefore, the image processing technology is required to further process the optical imaging picture so as to solve the problems of color degradation, low contrast, blurred details and the like of the underwater image.
Many researchers at home and abroad have mentioned many methods for underwater image processing, such as retinex image enhancement based on a variational framework, underwater image fusion, contrast-limited adaptive histogram equalization and other methods, but it is difficult to achieve better balance in processing effect and processing time, underwater image fusion and retinex image enhancement can achieve better enhancement effect in turbid water, but real-time performance in processing speed is difficult to achieve; the contrast-limited adaptive histogram equalization can achieve real-time performance, but the processing effect on some turbid water areas is not ideal.
Therefore, it is urgently required to provide an underwater image processing method which has both good processing effect and processing real-time performance.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: provided are a method and a storage medium capable of processing a muddy water image in real time, which can realize real-time processing and achieve a good processing effect.
In order to solve the technical problems, the invention adopts the technical scheme that:
1. a method of processing muddy water images in real time, comprising:
s1: correcting color cast of the input muddy water image;
s2: converting the corrected muddy water image from an RGB space to an LAB space, and acquiring a corresponding brightness component L, a color component A and a color component B;
s3: carrying out automatic contrast adjustment and sharpening on the brightness component L in sequence to obtain a processed brightness component L;
s4: the processed luminance component L, the color component a and the color component B are combined and converted back to the RGB space.
The invention provides another technical scheme as follows:
a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of a method of processing images of turbid water in real time as described above.
The invention has the beneficial effects that: based on that muddy water in most river water is mainly yellow green, and the gray value of a blue channel is lower. The invention sequentially carries out linear stretching in the step S1 to better correct the color cast of the picture; converting the image color space into an LAB space through the step of S2, and performing subsequent processing by using an L component to reduce the data amount of processing, and can keep the image color information unchanged, thereby realizing the speed of image processing and supporting real-time processing; the step S3 is used for carrying out automatic contrast adjustment successively, so that the contrast of the image is effectively enhanced, the detail information of the image is enriched, the local detail blur of the image is removed through sharpening, and the contrast of the image is further enhanced; and then the processed L component, the unprocessed A component and the unprocessed B component are merged to restore to be colorful, and are converted back to the RGB color space, and finally, the image quality effects of clear image details, high contrast and color fidelity are obtained.
Drawings
FIG. 1 is a schematic flow chart of a method for processing a muddy water image in real time according to the present invention;
FIG. 2 is a gray scale display of an unprocessed image of a turtle captured using an in-water imaging technique;
FIG. 3 shows the gray scale display effect of the image of the turtle processed according to the invention in FIG. 2;
FIG. 4 is a gray scale display of an unprocessed sea floor image taken using an in-water imaging technique;
FIG. 5 is a gray scale display of the sea floor image obtained after the processing of the present invention corresponding to FIG. 4;
FIG. 6 shows the gray scale display effect of an unprocessed color card image;
fig. 7 shows the gray scale display effect of the color card image obtained by the processing of the present invention corresponding to fig. 6.
Detailed Description
In order to explain technical contents, achieved objects, and effects of the present invention in detail, the following description is made with reference to the accompanying drawings in combination with the embodiments.
The most key concept of the invention is as follows: firstly correcting color cast, then converting the image into an LAB space, only performing subsequent image effect enhancement processing on the component L, and finally combining the components and converting the components back into an RGB space, thereby having real-time processing and good image effect.
The technical terms related to the invention are explained as follows:
referring to fig. 1, the present invention provides a method for processing a muddy water image in real time, comprising:
s1: correcting color cast of the input muddy water image;
s2: converting the corrected muddy water image from an RGB space to an LAB space, and acquiring a corresponding brightness component L, a color component A and a color component B;
s3: carrying out automatic contrast adjustment and sharpening on the brightness component L in sequence to obtain a processed brightness component L;
s4: the processed luminance component L, the color component a and the color component B are combined and converted back to the RGB space.
Further, the S1 specifically includes: and (4) processing the color cast of the input muddy water image according to the color correction of a statistical method.
Further, the S1 specifically includes:
s11: calculating respective mean values and root-mean-square errors of RGB three-color channels corresponding to the input muddy water image;
s12: calculating to obtain an upper limit value and a lower limit value in the corrected color cast mapping according to the respective mean value and the root mean square error;
s13: mapping the upper limit value and the lower limit value to obtain new gray level graphs of the RGB three-color channels corresponding to the muddy water image;
s14: and obtaining a muddy water image after correcting color cast according to the new gray level image.
As can be seen from the above description, in a specific embodiment, the color correction is performed by counting the shift degree of the gray level of the pixel point of each channel, so as to significantly improve the color shift phenomenon of the image.
Further, the automatic contrast adjustment specifically includes:
s31: performing histogram statistics on the luminance component L;
s32: solving an upper limit value and a lower limit value of a gray value corresponding to a certain pixel number in a histogram of the brightness component L;
s33: and carrying out image gray mapping transformation according to the upper limit value and the lower limit value of the gray value.
As can be seen from the above description, in a specific embodiment, an automatic contrast adjustment can be implemented by using a gray histogram mapping manner, so as to effectively enhance the contrast of an image and enrich the detailed information of the image.
Further, the certain number of pixels is 0.5% -2% of the total number of pixels of the histogram.
As can be seen from the above description, setting the number of pixels corresponding to the upper and lower limit values of the gray scale value to 0.5% -2% of the total number of pixels can make the processing effect more general.
Further, the sharpening process specifically includes:
s34: carrying out mean value filtering on the brightness component L after automatic contrast adjustment to obtain a low-frequency component S of the brightness component Ll；
S35: subtracting the low frequency component S from the automatically contrast adjusted luminance component LlObtaining a high frequency component S of the luminance component Lh；
S36: using the formula
Solving sharpened brightness component L image S'L(ii) a Wherein, the Amont is an adjusting coefficient; said SLThe luminance component L image after the automatic contrast adjustment.
As can be seen from the above description, in a specific embodiment, the local detail blur of the image can be removed by the above specific sharpening process, and the contrast of the image can be further enhanced, so as to obtain an excellent image processing effect.
Further, the numerical range of the adjustment coefficient Amont is 0.5 to 5.
As can be seen from the above description, in one embodiment, the adjustment coefficient Amont is set to 0.5-5, which can achieve better adjustment effect.
The invention provides another technical scheme as follows:
a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs all the steps included in the method for processing a muddy water image in real time according to any one of the above embodiments or a combination of the above embodiments.
From the above description, the beneficial effects of the present invention are: it should be understood by those skilled in the art that all or part of the processes in the above technical solutions may be implemented by instructing the related hardware through a computer program, where the program may be stored in a computer-readable storage medium, and when executed, the program may include the processes of the above methods.
The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Example one
Referring to fig. 1 to 7, the present embodiment provides a method for processing a muddy water image in real time, which can significantly improve the color cast of the image, enhance the detail and contrast, and improve the recognition capability of the underwater object, so that the image is more suitable for human eyes to watch, i.e., a good image processing effect can be obtained; meanwhile, the processing efficiency of the image can be obviously improved, so that the image processing method has real-time processing capability.
Referring to the flow chart of fig. 1, the method may specifically include the following steps:
the method comprises the following steps: adopting color correction based on a statistical method to the input muddy water image to process the color cast of the image;
specifically, the implementation process of this step may include:
the mean value and the root mean square error of the RGB three-color channels of the input image are calculated firstly, the upper limit value and the lower limit value in the color cast correction mapping are calculated accordingly, then the new gray level images of the three channels are obtained by utilizing the mapping of the upper limit value and the lower limit value, namely the gray level images of the three channels are obtained by mapping transformation. The specific formula is as follows:
wherein S' is the mapped gray value; s is a gray value before mapping; sminThe image gray scale lower limit value; smaxThe image gray scale upper limit value;
namely, color correction is carried out by counting the deviation degree of the gray value of the pixel point of each channel. By linearly stretching the different coefficients set by each channel, the color cast condition of the picture can be corrected better.
Step two: changing the color space of the image after color correction from an RGB space to an LAB space;
specifically, a color-displayed muddy water image is converted into a gray-scale-displayed muddy water image, and a brightness component L, a color component a and a color component B corresponding to the color-corrected muddy water image are obtained respectively.
The color space is converted into the LAB space through the step, and only the brightness component L is analyzed and processed, so that the data amount required to be processed can be obviously reduced, the color information of the image can be ensured not to be changed, and the real-time processing is further realized.
Step three: performing histogram statistics on the brightness component L;
step four: solving an upper limit value and a lower limit value of a gray value corresponding to a certain pixel number in the L component histogram;
preferably, the certain number of pixels is 0.5% -2% of the total number of pixels, so that the processing effect is more universal.
Step five: performing image gray mapping according to the upper limit value and the lower limit value of the gray value obtained in the previous step;
through the gray level histogram mapping from the third step to the fifth step, the contrast of the image can be effectively enhanced, and the detail information of the image is enriched.
Step six: sharpening the brightness component L after mapping transformation in the last step;
specifically, the implementation process of this step may include:
firstly, mean filtering is carried out on the brightness component L after mapping transformation to obtain a low-frequency component S of the L componentl(ii) a Then the L component is used for subtracting the low frequency component diagram to obtain a high frequency component S in the L componenth(ii) a The sharpened image is further solved by the following formula:
wherein, the SLThe luminance component L image is subjected to automatic contrast adjustment; amont is an adjustment coefficient, and preferably, the value thereof is set in the range of 0.5 to 5 to obtain a preferable effect.
And sixthly, sharpening the image subjected to the previous processing, so that local detail blurring of the image can be removed, and the contrast of the image can be further enhanced.
Step seven: and combining the brightness component L processed in the previous step and the A component and the B component which are not processed in the second step to restore a color image, and finally converting the color image back to an RGB color space for display.
Step eight: and outputting the image processed in the step seven.
The comparison between the image effect obtained by the image processing method of the present embodiment and the effect before processing is specifically shown in fig. 2 to 7. It should be particularly noted that, because the drawings in the specification of the application document specified in the patent law cannot be colored "color drawings", the images originally having colors can only be converted into "gray scale" for display to meet the requirements, and the conversion will certainly affect the more significant contrast effect before and after processing, and is expected to be known; however, even if the processing based on the gray scale display is compared in sequence, the obvious difference in image effect before and after the processing can be obviously seen.
Example two
This embodiment corresponds to the first embodiment, and provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps included in the method for processing a muddy water image in real time according to the first embodiment. The details of the steps will not be repeated, and the details will be described in the first embodiment.
In conclusion, the method and the computer storage mechanism for processing the muddy water image in real time provided by the invention can not only obtain the image quality effects of clear image details, high contrast and true color; the processing process is quick and efficient, and real-time processing and good image effect can be achieved; furthermore, the method also has the advantage of more universal processing effect, and the picture is more suitable for most of human eye watching requirements.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent changes made by using the contents of the present specification and the drawings, or applied directly or indirectly to the related technical fields, are included in the scope of the present invention.
Claims (6)
1. A method for processing a muddy water image in real time, comprising:
s1: correcting color cast of the input muddy water image;
s2: converting the corrected muddy water image from an RGB space to an LAB space, and acquiring a corresponding brightness component L, a color component A and a color component B;
s3: carrying out automatic contrast adjustment and sharpening on the brightness component L in sequence to obtain a processed brightness component L;
s4: combining the processed luminance component L, the color component A and the color component B and converting the combined luminance component L, the color component A and the color component B back to an RGB space;
the automatic contrast adjustment in step S3 is specifically:
s31: performing histogram statistics on the luminance component L;
s32: solving an upper limit value and a lower limit value of a gray value corresponding to a certain pixel number in a histogram of the brightness component L;
s33: carrying out image gray mapping transformation according to the upper limit value and the lower limit value of the gray value;
the sharpening process in step S3 specifically includes:
s34: carrying out mean value filtering on the brightness component L after automatic contrast adjustment to obtain a low-frequency component S of the brightness component Ll；
S35: subtracting the low frequency component S from the automatically contrast adjusted luminance component LlObtaining a high frequency component S of the luminance component Lh；
S36: using the formula
Solving sharpened brightness component L image S'L(ii) a Wherein, the Amount is an adjustment coefficient; said SLThe luminance component L image after the automatic contrast adjustment.
2. The method according to claim 1, wherein the step S1 is embodied as: and (4) processing the color cast of the input muddy water image according to the color correction of a statistical method.
3. The method of claim 2, wherein the step S1 specifically comprises:
s11: calculating respective mean values and root-mean-square errors of RGB three-color channels corresponding to the input muddy water image;
s12: calculating to obtain an upper limit value and a lower limit value in the corrected color cast mapping according to the respective mean value and the root mean square error;
s13: mapping the upper limit value and the lower limit value to obtain new gray level graphs of the RGB three-color channels corresponding to the muddy water image;
s14: and obtaining a muddy water image after correcting color cast according to the new gray level image.
4. The method of claim 1, wherein the certain number of pixels is 0.5% -2% of the total number of pixels in the histogram.
5. The method of claim 1, wherein the adjustment coefficient Amount is in a range of 0.5 to 5.
6. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of a method of processing images of turbid water in real time according to any one of claims 1 to 5.
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| CN109886885B (en) * | 2019-01-23 | 2021-03-02 | 齐鲁工业大学 | Image enhancement method and system based on Lab color space and Retinex |
| CN109934805B (en) * | 2019-03-04 | 2023-05-09 | 江南大学 | Water pollution detection method based on low-illumination image and neural network |
| CN110796626B (en) * | 2019-11-13 | 2022-08-02 | 中国电子科技集团公司信息科学研究院 | Image sharpening method and device |
| CN111225278A (en) * | 2020-03-02 | 2020-06-02 | 新疆大学 | Method and device for enhancing video under low illumination |
| CN111291839A (en) * | 2020-05-09 | 2020-06-16 | 创新奇智(南京)科技有限公司 | Sample data generation method, device and equipment |
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