CN110473155B - Image defogging method based on retina color perception dark channel principle - Google Patents
Image defogging method based on retina color perception dark channel principle Download PDFInfo
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- 230000007246 mechanism Effects 0.000 claims abstract description 8
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- 230000036755 cellular response Effects 0.000 claims description 18
- 230000035945 sensitivity Effects 0.000 claims description 12
- 210000004027 cell Anatomy 0.000 claims description 10
- 210000003569 retinal bipolar cell Anatomy 0.000 claims description 9
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- 230000002207 retinal effect Effects 0.000 claims description 7
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Abstract
The invention discloses an image defogging method based on a retina color perception dark channel principle, which comprises the following steps: s1: acquiring an original fog-containing image; s2: calculating the foggy image in the step S1 by utilizing a retina color perception mechanism, and calculating three color channels of the color foggy image; s3: restoring the fog-containing image obtained in the step S2 by adopting a transmissivity estimation function, an atmospheric light estimation function and a restoration function to obtain a final output image; the invention solves the problems of low self-adaptation capability and further improvement of the visual quality of the image in the existing image defogging technology, and the method has simple process and is suitable for restoration of any foggy image.
Description
Technical Field
The invention belongs to the technical field of computer image processing, and particularly relates to an image defogging method based on a retina color perception dark channel principle.
Background
With the rapid development of computer vision technology and hardware sensor technology, various image or video acquisition technologies have been widely applied to important fields such as virtual battlefield, intelligent medical treatment, aerospace, traffic monitoring and public security. The colorful visual information is reproduced on the electronic equipment, the visual feeling is better in the aspect of reality, and the accurate visual information is better in the aspect of application.
In recent years, the demand of human beings for sensing the outside is increasingly increased, various image acquisition technologies are continuously developed, and more convenient conditions are provided for acquiring visual information, however, in the haze weather, as the visibility of objects in the environment is reduced, the color information and contrast characteristics of the objects are scattered and absorbed by suspended particles in the air, so that the quality of images acquired in foggy days is reduced, the processing difficulty of the images is further increased, and the method is particularly suitable for target scenes which are difficult to reproduce in aerospace, traffic monitoring, public safety and the like. Therefore, image defogging has become an important research direction for computer vision.
The image defogging method aims at reducing the influence of haze on the image, improving the quality of the image and obtaining the image with higher signal-to-noise ratio and larger information entropy. So far, the image defogging method is mainly based on two directions of image enhancement and physical model. Aiming at the degraded image, an image enhancement means is adopted to improve the visual effect of the image and the demand of a computer visual system, the method does not consider the cause of the degraded image, can not be generally used in any scene, and has low self-adaptive capacity; the method aims at the degradation image cause, an image degradation model is physically constructed, and the method achieves a good visual effect by combining the degradation image cause and essential defogging.
Disclosure of Invention
The invention aims to provide an image defogging method based on a retina color perception dark channel principle, which comprises the following steps of: s1: acquiring an original fog-containing image; s2: calculating the foggy image in the step S1 by utilizing a retina color perception mechanism, and calculating three color channels of the color foggy image; s3: and (2) restoring the foggy image obtained in the step (S2) by adopting a transmissivity estimation function, an atmospheric light estimation function and a restoration function to obtain a final output image, thereby solving the problems of low self-adaption capability and further improvement of the visual quality of the image in the existing image defogging technology.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
an image defogging method based on a retina color perception dark channel principle comprises the following steps:
s1: acquiring an original fog-containing image;
s2: calculating the foggy image in the step S1 by utilizing a retina color perception mechanism, and calculating three color channels of the color foggy image;
s3: and (2) restoring the fog-containing image obtained in the step (S2) by adopting a transmissivity estimation function, an atmospheric light estimation function and a restoration function to obtain a final output image.
Further, the calculation process in step S2 includes the steps of:
(1) The retinal bipolar cell response was calculated as follows:
(2) Retinal ganglion cell response, calculated as follows:
(3) Based on the perception mechanism of the retina receptive field to the color in the visual system, respectively performing color perception calculation aiming at three color channels of the color image, wherein a calculation model is as follows:
wherein alpha is cen Is the sensitivity around the receptive field of bipolar cells;
α sur sensitivity at the center of the bipolar receptive field;
(x, y) is the position coordinates of the image;
I c (x, y) is the raw foggy image pixel value obtained in step S1;
σ cen 、σ sur is the standard deviation of the gaussian function;
BF c cen is the cellular response of retinal bipolar cells in the central receptive field;
BF c sur is the cellular response of retinal bipolar cells in the surrounding receptive field;
RGC c cen is the cellular response of retinal ganglion cells in the central receptive field;
RGC c sur is the cellular response of retinal ganglion cells to the surrounding receptive field;
β cen is the sensitivity around ganglion cell receptive fields;
β sur is the sensitivity of the ganglion cell receptive field center.
Further, the image restoration process in step S3 includes the steps of:
(1) Transmittance estimation
According to the foggy day imaging mode, the transmittance estimation calculation formula is as follows:
for each color channel, the following formula is calculated:
according to the dark channel prior principle, the following formula is calculated:
then the first time period of the first time period,
in combination with the retinal color perception calculation model in step S2, there are the following calculation formulas:
(2) Atmospheric light estimation
The estimate of atmospheric light Ac is calculated as follows:
(3) Foggy image restoration
The foggy image recovery is calculated as follows:
wherein I is c (x) Is an input color image;
J c (x) Is an defogged image;
A c is atmospheric light;
t (x) is a transmission image;
t (x) =exp { - αd (x) }, α is the atmospheric diffusion coefficient, D (x) is the depth of field;
ζ is an image enhancement coefficient;
I c is the light intensity of the input color image;
R c (x) Is a response of retinal perception;
σ c is a constant;
t 0 is a factor that constrains the transmission t (x) to be non-zero, taking a value of 0.1.
Specifically, in step S2, α cen Alpha and alpha sur Take a value of 1, sigma cen Sigma (sigma) sur Take the value of 1, beta cen Beta and beta sur The value is 1.
Specifically, in step S3, α has a value of 1, d (x) has a value of 0.95, ζ has a value of 1, σ c Take the value of 1, t 0 The value is 0.1.
The invention has the beneficial effects that the retina color perception calculation and dark channel principle is adopted to carry out image defogging treatment on the image acquired in foggy weather environment, and the image defogging treatment is output, so that the problems of low self-adaption capability and further improvement of image vision quality in the existing image defogging technology are solved.
Drawings
In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an image defogging method of the present invention;
FIG. 2 is an image of the defogging process in example 1 of the present invention;
FIG. 3 is a view of the original unagglomerated color image obtained in example 2 of the present invention;
FIG. 4 is a color misted image after artificial misting in example 2 of the invention;
FIG. 5 is a color image of example 2 of the present invention after defogging by the method of the present invention.
Detailed Description
In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
The present invention will be described in detail with reference to the accompanying drawings and examples.
The method of the present invention is implemented using MatlabR2018 programming in the following examples. The experimental platform is mainly configured as follows: the operating system is Windows10, the CPU is Intel Corei75600U, and the RAM is 8G.
Example 1
An image defogging method based on the principle of retina color perception dark channel takes a pair of true color foggy images as an example, and comprises the following steps:
s1: acquiring an original foggy image, wherein the original foggy image mainly comes from a real test image data set which is common at home and abroad, as shown in fig. 2;
s2: calculating the foggy image in the step S1 by utilizing a retina color perception mechanism, and calculating three color channels of the color foggy image, wherein the calculating process comprises the following steps:
(1) The retinal bipolar cell response was calculated as follows:
(2) Retinal ganglion cell response, calculated as follows:
(3) Based on the perception mechanism of the retina receptive field to the color in the visual system, respectively performing color perception calculation aiming at three color channels of the color image, wherein a calculation model is as follows:
wherein alpha is cen The sensitivity around the bipolar cell receptive field is 1;
α sur the sensitivity of the center of the bipolar cell receptive field is 1;
(x, y) is the position coordinates of the image;
I c (x, y) is the raw foggy image pixel value obtained in step S1;
σ cen 、σ sur is the standard deviation of Gaussian function, sigma cen Sigma (sigma) sur The values are all 1;
BF c cen is the cellular response of retinal bipolar cells in the central receptive field;
BF c sur is the cellular response of retinal bipolar cells in the surrounding receptive field;
RGC c cen is the cellular response of retinal ganglion cells in the central receptive field;
RGC c sur is the cellular response of retinal ganglion cells to the surrounding receptive field;
β cen the sensitivity around ganglion cell receptive field is 1;
β sur the sensitivity of the center of the ganglion cell receptive field is 1;
s3: and (2) restoring the foggy image obtained in the step (S2) by adopting a transmissivity estimation function, an atmospheric light estimation function and a restoration function to obtain a final output image, wherein the image restoration process comprises the following steps of:
(1) Transmittance estimation
According to the foggy day imaging mode, the transmittance estimation calculation formula is as follows:
for each color channel, the following formula is calculated:
according to the dark channel prior principle, the following formula is calculated:
then the first time period of the first time period,
in combination with the retinal color perception calculation model in step S2, there are the following calculation formulas:
(2) Atmospheric light estimation
The estimate of atmospheric light Ac is calculated as follows:
(3) Foggy image restoration
The foggy image recovery is calculated as follows:
wherein I is c (x) Is an input color image;
J c (x) Is an defogged image;
A c is atmospheric light;
t (x) is a transmission image;
t (x) =exp { - αd (x) }, α is the atmospheric diffusion coefficient, D (x) is the depth of field;
ζ is an image enhancement coefficient;
I c is the light intensity of the input color image;
R c (x) Is a response of retinal perception;
σ c is a constant;
t 0 is a factor that constrains the transmission t (x) to be non-zero, taking a value of 0.1.
Example 2
An image defogging method based on a retina color perception dark channel principle takes a color foggy image after artificial atomization as an example, wherein the color foggy image which is not infected by fog is obtained as shown in fig. 3, the color foggy image after artificial atomization is obtained as shown in fig. 4, the color foggy image after artificial atomization is recovered as shown in fig. 5, and the color foggy image after artificial atomization is derived from a common artificially synthesized test image data set at home and abroad.
In embodiment 2, the same steps and parameters as those in embodiment 1, that is, the same parameters as those in embodiment 1 are adopted in the retinal color perception calculation step 2, the same parameters as those in embodiment 1 are adopted in the image restoration based on the dark channel principle step 3, the same parameters as those in embodiment 1 are adopted in the image quality after defogging is objectively evaluated by objective quality evaluation on a color foggy image after artificial atomization by adopting a peak signal-to-noise ratio (Peak Signal to Noise Ratio, abbreviated as PSNR) as follows:
where MSE is the mean square error of two images, calculated as follows:
where fi, j is the original uncolored image, f' i, j is the atomized image, and M, N are the sizes of the images, respectively. The objective quality PSNR value before defogging is 10.71, and the PSNR value after defogging by the method is 17.26.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (3)
1. An image defogging method based on a retina color perception dark channel principle is characterized by comprising the following steps of:
s1: acquiring an original fog-containing image;
s2: calculating the foggy image in the step S1 by utilizing a retina color perception mechanism, and calculating three color channels of the color foggy image;
s3: restoring the fog-containing image obtained in the step S2 by adopting a transmissivity estimation function, an atmospheric light estimation function and a restoration function to obtain a final output image;
the calculation process of the step S2 includes the following steps:
(1) The retinal bipolar cell response was calculated as follows:
(2) Retinal ganglion cell response, calculated as follows:
(3) Based on the perception mechanism of the retina receptive field to the color in the visual system, respectively performing color perception calculation aiming at three color channels of the color image, wherein a calculation model is as follows:
wherein alpha is cen Is the sensitivity around the receptive field of bipolar cells;
α sur sensitivity at the center of the bipolar receptive field;
(x, y) is the position coordinates of the image;
I c (x, y) is the raw foggy image pixel value obtained in step S1;
σ cen 、σ sur is the standard deviation of the gaussian function;
BF c cen is the cellular response of retinal bipolar cells in the central receptive field; BF (BF) c sur Is the cellular response of retinal bipolar cells in the surrounding receptive field;
RGC c cen is the cellular response of retinal ganglion cells in the central receptive field; RGC (RGC) c sur Is the cellular response of retinal ganglion cells to the surrounding receptive field; beta cen Is the sensitivity around ganglion cell receptive fields;
β sur is the sensitivity of the center of the ganglion cell receptive field;
the image restoration process in the step S3 includes the following steps:
(4) Transmittance estimation
According to the foggy day imaging mode, the transmittance estimation calculation formula is as follows:
for each color channel, the following formula is calculated:
according to the dark channel prior principle, the following formula is calculated:
then the first time period of the first time period,
in combination with the retinal color perception calculation model in step S2, there are the following calculation formulas:
(5) Atmospheric light estimation
Atmospheric light A c Is calculated as follows:
(6) Foggy image restoration
The foggy image recovery is calculated as follows:
wherein I is c (x) Is an input color image;
J c (x) Is an defogged image;
A c is atmospheric light;
t (x) is a transmission image;
t (x) =exp { - αd (x) }, α is the atmospheric diffusion coefficient, D (x) is the depth of field;
ζ is an image enhancement coefficient;
I c is the light intensity of the input color image;
R c (x) Is a response of retinal perception;
σ c is a constant;
t 0 is a factor that constrains the transmission t (x) to be non-zero.
2. The image defogging method based on the principle of a dark channel perceived by a retina color according to claim 1, wherein in said step S2, α cen Alpha and alpha sur Take a value of 1, sigma cen Sigma (sigma) sur Take the value of 1, beta cen Beta and beta sur The value is 1.
3. The image defogging method according to claim 1, wherein in said step S3, alpha is 1, D (x) is 0.95, ζ is 1, σ c Take the value of 1, t 0 The value is 0.1.
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