CN112837233A - Polarization image defogging method for acquiring transmissivity based on differential polarization - Google Patents
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Abstract
The invention discloses a polarization image defogging method for acquiring transmissivity based on differential polarization, and belongs to the technical field of image defogging processing. The method of the invention is to use a polarization imaging method to obtain two orthogonal polarization images of the same scene. And establishing an atmospheric scattering model based on the physical model of the foggy image. And combining the bright channel prior and the dark channel prior to respectively calculate the bright channel image and the dark channel image of the two sub-images to obtain a transmittance image t1 of the dark channel and an atmospheric light value at infinity. According to the differential image defogging method, the optical thickness is replaced by the fog density approximation, and a differential transmittance map t2 is obtained. And combining the dark channel transmittance graph and the differential transmittance graph to obtain a total transmittance image, performing threshold processing on the total transmittance image, and performing refinement processing by using guide filtering. And substituting the obtained transmissivity and atmospheric light into an atmospheric scattering model to obtain a fog-free image. The invention obviously improves the image quality, obtains more object reflected light information and improves the definition of the restored image.
Description
Technical Field
The invention relates to a polarization image defogging method for acquiring transmissivity based on differential polarization, and belongs to the technical field of image defogging processing.
Background
In recent years, haze weather is increasingly aggravated, which causes the quality of images acquired by an imaging system to be reduced, and further seriously influences the analysis and judgment of a visual system; therefore, improving the definition of the foggy image to obtain better visual effect and richer detail information processing algorithms has become a key field of research. Currently, many researchers have proposed many methods for processing images in foggy days; atmospheric light formed by scattering solar light by haze and other scattering particles has partial polarization characteristics, so that the polarized optical imaging defogging method is suitable for various haze weathers of various scenes, has a wide application range and becomes one of research hotspots; under the strong scattering environment, the target detection and identification based on the polarization imaging technology has the advantages and the application which are not available in other imaging modes; the dark channel prior algorithm is not suitable for sky and large-area white areas, and the atmospheric light estimated value is easily overlarge, so that the color distortion problem is caused.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a polarization image defogging method based on differential polarization and combined with light and dark channel prior to obtaining transmittance, so as to overcome the defects of the prior art.
The technical scheme adopted by the invention is as follows: a polarized image defogging method based on differential polarization and combined with light and dark channel prior to obtaining transmittance comprises the following steps:
the method specifically comprises the following steps:
(1) imaging a target scene by using a polarization imaging device, rotating a polaroid to obtain two orthogonal polarization images of the same scene, and recording a light intensity image with the minimum influence of fog on the images as I//The intensity map with the greatest effect on the image is denoted as I⊥；
The atmospheric scattering model is established based on the physical model of the foggy image, and comprises two parts:
the scene target is directly transparent: d (x, y) ═ L (x, y) t (x, y)
Atmospheric light: b (x, y) ═ a∞[1-t(x，y)]
Where L (x, y) is the reflected light intensity of the target, i.e. the fog-free image, and t ═ e-βdBeta d is the optical thickness, A∞Atmospheric light value at infinity;
the total light intensity, i.e. the foggy image, is an incoherent superposition of the two, i.e.:
I(x，y)＝D(x，y)+B(x，y)＝L(x，y)t(x，y)+A∞[1-t(x，y)]
the target reflected light intensity can be obtained:
(2) inputting two orthogonal polarization images, combining bright and dark channel prior, respectively calculating bright and dark channel images of two sub-images, and obtaining a transmittance image t1 of a dark channel and an atmospheric light value at infinity;
(3) according to the difference image defogging method, the fog concentration is approximately used for replacing the optical thickness, and then a difference transmittance graph t2 is obtained;
(4) combining the dark channel transmittance graph with the differential transmittance graph to obtain a total transmittance image, performing threshold processing on the total transmittance image, and performing thinning processing by using guide filtering to obtain an optimized transmittance image;
(5) substituting the obtained total transmittance image and the obtained atmospheric scattering image into an atmospheric scattering model to obtain a defogging restored image, namely:
further, the dark channel prior theory in step (2) of the present invention is as follows: for most images without sky areas, the pixel value of at least one of the three color channels of all the pixel points of the image block is the lowest, and a dark channel image is defined as follows:
wherein IcRepresenting the color channel of the image I, wherein omega (x) represents an image block taking a pixel point x as the center, counting finds that the image is out of the sky area and a dark channel IdarkThe value is extremely low and approaches to zero, so that a dark channel transmittance image can be obtained
Omega is an adjusting parameter;
similar to dark channel prior, the pixel value for the existing channel is very large, approaching 1, and the bright channel image is defined as:
the image block is maximized, the channel is maximized, and the bright channel of any pixel point is close to the atmospheric light value A of the fog-free imagelight：Ilight→Alight(ii) a The atmospheric light value is estimated by combining the two sub-images as:
where m, n are the size of the image.
Further, step (4) of the present invention includes the following steps: and respectively carrying out sum and difference processing on the two sub-graphs to obtain a total light intensity graph I-I//+I⊥And polarization difference diagram Δ I ═ I//-I⊥(ii) a Degree of image polarization
Atmospheric light polarization degree:
based on the polarization characteristics of light, it can be approximately considered that the polarization state of light waves reaching the polarization imaging system is mainly caused by atmospheric light, so that there are:
therefore, when Δ B is equal to Δ I, it is possible to obtain
When the transmittance is determined, the atmospheric light is deformed: b (x, y) ═ a∞[1-t(x，y)]→B(x，y)＝A∞H(x，y)， 
Wherein P isscattP, H (x, y) is the haze concentration, H (x, y) 1-t (x, y) 1-e-βdSince H ∈ β d, the differential image transmittance can be determined by roughly replacing the optical thickness of β d with H:
t2(x，y)＝e-η*H(x，y)。
further, the total transmission in step (5) of the present invention is: t is alpha t1+ beta t2, and alpha and beta are correction coefficients; obtaining an atmospheric scattering image B (x, y) ═ A by combining an atmospheric scattering model∞[1-t(x，y)](ii) a In order to avoid the pixel value of the calculated transmissivity from being too low, threshold processing is carried out on the total transmissivity, and pixel points with the pixel values smaller than 0.1 are set to be 0.1; and taking the total image obtained by performing the processing on the two sub-images as a guide filtering image, and performing guide filtering processing on the transmittance image to obtain an optimized transmittance image.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, the polarized differential imaging and the bright-dark channel prior algorithm are combined, and compared with the traditional dark channel algorithm, the quality of the restored image is obviously improved, namely the problems of color distortion and blocking effect after dark channel prior processing do not exist, the detail information is richer, more image contents are obtained, and the definition of the restored image is improved.
Drawings
FIG. 1 is a flow chart of the algorithm of the present invention;
FIG. 2(a) is a fog image to be processed for orthogonal image synthesis;
FIG. 2(b) is a restored image of difference algorithm defogging;
FIG. 2(c) is a restored image of the dark channel algorithm defogging;
fig. 2(d) is a restored image of the defogging by the algorithm herein.
Detailed Description
The invention will be further described with reference to the drawings and the embodiments, but the scope of the invention is not limited thereto.
Example 1
A polarized image defogging method based on differential polarization and combined with light and dark channel prior to obtaining transmissivity specifically comprises the following steps:
(1) imaging a target scene by using a polarization imaging method and equipment, and rotating a polaroid to obtain two orthogonal polarization images of the same scene; respectively, the intensity map (denoted as I) in which the fog has the least influence on the image//) And the intensity map (denoted as I) that has the greatest effect on the image⊥) (ii) a Two orthogonal images were read in MATLAB (both steps below were implemented on the MATLAB platform).
According to the imaging characteristics of the scattering environment, the atmospheric scattering model comprises two parts:
the scene target is directly transparent: d (x, y) ═ L (x, y) t (x, y)
Atmospheric light: b (x, y) ═ a∞[1-t(x，y)]
Where (x, y) represents the coordinates of the pixel in the diagram, L (x, y) is the target reflected light intensity, i.e. fog-free image, and t is e-βdBeta d is the optical thickness, A∞Is the atmospheric light value at infinity.
The total light intensity (i.e. the foggy image) is the incoherent superposition of the two, i.e.:
I(x，y)＝D(x，y)+B(x，y)＝L(x，y)t(x，y)+A∞[1-t(x，y)]
the target reflected light intensity can be obtained:
(2) obtaining dark channel images of two subgraphs according to a dark channel prior theory: for most images without sky areas, the pixel value of at least one of the three color channels of all the pixel points of the image block is the lowest; define dark channel images as:
wherein IcRepresenting the color channel of the image I, wherein omega (x) represents an image block taking a pixel point x as the center, counting finds that the image is out of the sky area and a dark channel IdarkVery low value (here I)darkThe sum of two sub-dark channel maps) approaches zero, so that the coarse image transmittance can be obtained
ω 0.95 is an adjustment parameter.
Similar to dark channel prior, the pixel value for the existing channel is very large, approaching 1, and the bright channel image is defined as:
the image block is maximized, the channel is maximized, and the bright channel of any pixel point is close to the atmospheric light value A of the fog-free imagelight：Ilight→Alight. The atmospheric light value is estimated by combining the two sub-images as follows:
where m, n are the size of the image.
And respectively carrying out sum and difference processing on the two subgraphs to obtain a total light intensity graph (to-be-processed haze graph) I ═ I//+I⊥And polarization difference diagram Δ I ═ I//-I⊥(ii) a Degree of image polarization
Atmospheric light polarization degree:
based on the polarization characteristics of light, it can be approximately considered that the polarization state of light waves reaching the polarization imaging system is mainly caused by atmospheric light, so that there are:
therefore, when Δ B is equal to Δ I, it is possible to obtain
When the transmittance is determined, the atmospheric light is deformed: b (x, y) ═ a∞[1-t(x，y)]→B(x，y)＝A∞H(x，y)， 
Wherein P isscattP, H (x, y) is the haze concentration, H (x, y) 1-t (x, y) 1-e-βdSince H ∈ β d, the differential image transmittance can be determined by roughly replacing the optical thickness of β d with H:
t2(x，y)＝e-η*H(x，y)η is set to 0.9.
The total transmission was: t is α t1+ β t2, α and β are correction coefficients, α is 0.4, and β is 0.9. Obtaining an atmospheric scattering image B (x, y) ═ A by combining an atmospheric scattering model∞[1-t(x，y)]Then, in order to avoid the pixel value of the calculated transmittance from being too low, threshold processing is performed on the total transmittance, and the pixel point with the pixel value less than 0.1 is set to be 0.1.
In order to avoid the block effect of the transmissivity t and the loss of image information, the total image obtained by performing the processing on the two sub-images is used as a guide filtering image, and the guide filtering processing is performed on the transmissivity image; specifically, the coarse transmittance t is used as an input image p of the guide filtering, and the guide image I needs to have the same edge information as the input image, so that the images are processed by using the original images to be processed respectively to obtain optimized transmittance images.
And finally, substituting the optimized transmittance image and the optimized atmosphere scattering image into an atmosphere scattering model to obtain a defogged restored image, namely:
in conjunction with FIG. 2Compared with the images processed by other methods, the original image in (1) can be seen from three places A, B, C, and the image information restored by the processing result of the method is more and has no color distortion problem.
Claims (4)
1. A polarization image defogging method for acquiring transmittance based on differential polarization is characterized by comprising the following steps:
(1) imaging a target scene by using a polarization imaging device, rotating a polaroid to obtain two orthogonal polarization images of the same scene, and recording a light intensity image with the minimum influence of fog on the images as I//Intensity map with the greatest effect on the image, denoted I⊥；
The atmospheric scattering model is established based on the physical model of the foggy image, and comprises two parts:
the scene target is directly transparent: d (x, y) ═ L (x, y) t (x, y)
Atmospheric light: b (x, y) ═ a∞[1-t(x，y)]
Where L (x, y) is the reflected light intensity of the target, i.e. the fog-free image, and t ═ e-βdBeta d is the optical thickness, A∞Atmospheric light value at infinity;
the total light intensity, i.e. the foggy image, is an incoherent superposition of the two, i.e.:
I(x，y)＝D(x，y)+B(x，y)＝L(x，y)t(x，y)+A∞[1-t(x，y)]
the target reflected light intensity can be obtained:
(2) inputting two orthogonal polarization images, combining bright and dark channel prior, respectively calculating bright and dark channel images of two sub-images, and obtaining a transmittance image t1 of a dark channel and an atmospheric light value at infinity;
(3) according to the difference image defogging method, the fog concentration is approximately used for replacing the optical thickness, and then a difference transmittance graph t2 is obtained;
(4) combining the dark channel transmittance graph with the differential transmittance graph to obtain a total transmittance image, performing threshold processing on the total transmittance image, and performing refinement processing by using guide filtering to obtain an optimized transmittance image;
(5) substituting the obtained total transmittance image and the obtained atmospheric scattering image into an atmospheric scattering model to obtain a defogging restoration image, namely:
2. the polarization image defogging method according to claim 1, wherein said transmittance is obtained based on differential polarization, and said method comprises: the dark channel prior theory in the step (2): for most images without sky areas, the pixel value of at least one of the three color channels of all the pixel points of the image block is the lowest, and a dark channel image is defined as follows:
wherein IcRepresenting the color channel of the image I, wherein omega (x) represents an image block taking a pixel point x as the center, counting finds that the image is out of the sky area and a dark channel IdarkThe value is extremely low and approaches to zero, so that a dark channel transmittance image can be obtained
Omega is an adjusting parameter;
similar to dark channel prior, the pixel value for the presence of one channel is very large, approaching 1, and the bright channel image is defined as:
the image block is maximized, the channel is maximized, and the bright channel of any pixel point is close to the atmospheric light value A of the fog-free imagelight：Ilight→Alight(ii) a The atmospheric light value is estimated by combining the two sub-images as:
where m, n are the size of the image.
3. The polarization image defogging method according to claim 1, wherein said transmittance is obtained based on differential polarization, and said method comprises: the step (3) comprises the following steps: and respectively carrying out sum and difference processing on the two sub-graphs to obtain a total light intensity graph I-I//+I⊥And polarization difference diagram Δ I ═ I//-I⊥(ii) a Degree of image polarization
Atmospheric light polarization degree:
based on the polarization characteristics of light, it can be approximately considered that the polarization state of light waves reaching the polarization imaging system is mainly caused by atmospheric light, so that there are:
therefore, when Δ B is equal to Δ I, it is possible to obtain
When the transmittance is determined, the atmospheric light is deformed: b (x, y) ═ a∞[1-t(x，y)]→B(x，y)＝A∞H(x，y)，
Wherein P isscattP, H (x, y) is the haze concentration, H (x, y) 1-t (x, y) 1-e-βdSince H. varies.. beta.d, it is possible to obtain the optical thickness of beta.d by roughly replacing H with the optical thickness of beta.dDifferential image transmittance:
t2(x，y)＝e-η*H(x，y)。
4. the polarization image defogging method according to claim 1, wherein said transmittance is obtained based on differential polarization, and said method comprises: the total transmission in the step (4) is as follows: t is alpha t1+ beta t2, and alpha and beta are correction coefficients; obtaining an atmospheric scattering image B (x, y) ═ A by combining an atmospheric scattering model∞[1-t(x，y)](ii) a In order to avoid the pixel value of the calculated transmissivity from being too low, threshold processing is carried out on the total transmissivity, and pixel points with the pixel values smaller than 0.1 are set to be 0.1; and taking the total image obtained by performing the summation processing on the two sub-images as a guide filtering image, and performing guide filtering processing on the transmittance image to obtain an optimized transmittance image.
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| CN114324185A (en) * | 2022-01-04 | 2022-04-12 | 浙江大学 | Underwater polarization detection device based on Stokes vector |
| CN114841885A (en) * | 2022-05-10 | 2022-08-02 | 中国矿业大学(北京) | Defogging fusion processing method based on polarization image data |
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| CN114841885A (en) * | 2022-05-10 | 2022-08-02 | 中国矿业大学(北京) | Defogging fusion processing method based on polarization image data |
| CN116337088A (en) * | 2023-05-30 | 2023-06-27 | 中国人民解放军国防科技大学 | Foggy scene relative motion estimation method and device based on bionic polarization vision |
| CN116337088B (en) * | 2023-05-30 | 2023-08-11 | 中国人民解放军国防科技大学 | Foggy scene relative motion estimation method and device based on bionic polarization vision |
| CN116596805A (en) * | 2023-07-14 | 2023-08-15 | 山东大学 | Polarization defogging method based on polarization state difference of scene object and atmosphere light |
| CN116596805B (en) * | 2023-07-14 | 2023-09-29 | 山东大学 | Polarization defogging method based on polarization state difference of scene object and atmosphere light |
| CN117649439A (en) * | 2024-01-30 | 2024-03-05 | 山东省海洋资源与环境研究院(山东省海洋环境监测中心、山东省水产品质量检验中心) | Sea weed bed area acquisition method, system, equipment and storage medium |
| CN117649439B (en) * | 2024-01-30 | 2024-04-09 | 山东省海洋资源与环境研究院(山东省海洋环境监测中心、山东省水产品质量检验中心) | Sea weed bed area acquisition method, system, equipment and storage medium |
| CN117911282A (en) * | 2024-03-19 | 2024-04-19 | 华中科技大学 | Construction method and application of image defogging model |
| CN117911282B (en) * | 2024-03-19 | 2024-05-28 | 华中科技大学 | Construction method and application of image defogging model |
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