Patent Grant
11257194
The present application is based on International Application No. PCT/CN2018/125153, filed on Dec. 29, 2018, which claims priority to Chinese Patent Application No. 201810387076.2, filed on Apr. 26, 2018, the contents of which are incorporated herein by reference in their entireties.
The present disclosure relates to the technical field of image processing, and in particular, to a method for image dehazing based on adaptively improved linear global atmospheric light of a dark channel.
Since dust particles and water vapor in the air will absorb and scatter light in the haze weather, which causes light intensity received by a sensor to change, images captured in haze weather will be affected by the weather and thus has a reduced quality. The clarity of scenes located in a haze image is lower than that the clarity of scenes located in an image taken on fine days, which may result in limitation of some image-based applications, such as traffic safety monitoring and target recognition in aerial surveillance. The image dehazing technology can eliminate or reduce effect of the haze weather on the image quality, so it has practical significance.
At present, there are already some algorithms that can dehaze a single image, and these algorithms can be divided, in principle, into enhancement methods based on image processing and restoration methods based on physical models. The enhancement algorithms based on image processing include histogram equalization, a Retinex algorithm and the like, and the restoration algorithms based on physical models include a Tan algorithm, a Fattal algorithm, a He algorithm and the like.
In 2009, He et al. proposed, at the CVPR conference, single image haze removal using dark channel prior, achieved a better dehazing effect and then improved the algorithm accordingly. This algorithm applies a prior statistical law of the dark primary colors to a physical model, i.e., an atmospheric scattering model, successively calculates a dark primary color map, an atmospheric light value, a rough propagation map and a fine propagation map, and finally substitutes the atmospheric scattering model to obtain a dehazed image. However, after a lot of experiments, it is found that when the algorithm of He et al. is used to deal with images captured in dense haze, it is impossible to have both contrast of a dark area and details of a bright area after a selected atmospheric light value is used to process the image, due to uneven illumination.
An object of the present disclosure is to provide an method for image dehazing based on adaptively improved linear global atmospheric light of a dark channel, and it replaces the global atmospheric light value with the atmospheric light map to avoid the image distortion caused by an excessively large difference between the atmospheric light value of the dense haze part and the atmospheric light value of the close scene part after dehazing due to uneven distribution of the atmospheric light in dense haze weather. The present disclosure can not only maintain the contrast of the dark area of the image, but also can show the details of the scene in the bright area.
In order to achieve the object above, the present disclosure adopts a following technical solution.
A method for image dehazing based on adaptively improved linear global atmospheric light of a dark channel, includes:
step 1: obtaining a haze image in haze weather;
step 2: performing threshold segmentation on the haze image obtained in the step 1 to obtain a binary image;
step 3: obtaining a center of gravity (x0 y0) of the image and a center (0.5*h 0.5*w) of the image for the binary image obtained in the step 2, where h is height of the binary image and w is width of the binary image, then performing normalization by dividing horizontal coordinates and vertical coordinates corresponding to the center of gravity and the image center of the binary image by h and w respectively, so as to obtain a center of gravity (x0′ y0′) and a center (0.50.5), where k is defined as a slope and θ is defined as a deflection angle of an atmospheric light value, where
and θ=arctan(1/k);
step 4: calculating the dark channel of the haze image I obtained in the step 1:
Idark(x,y)=minC∈{r,g,b}(min(x′,y′)∈Ω(x,y)(IC(x′,y′)))
where Ω(x, y) represents a window of neighborhood of a point (x, y), Idark′(x, y) represents a dark channel image, and IC(x′,y′) represents a monochrome channel image pixel of the haze image I,
rotating the image dark channel Idark(x, y) counterclockwise according to the deflection angle θ obtained in the step 3, to obtain the rotated dark channel Idark′(x, y);
step 5: obtaining an evenly varied atmospheric light map A′(x, y) for the dark channel obtained in the step 4;
step 6: counterclockwise rotating the evenly varied atmospheric light map A′(x, y) obtained in the step 5 according to the atmospheric light value deflection angle θ obtained in the step 3, to obtain a final atmospheric light map A (x, y), which is distributed regularly according to a variation direction of the concentration degree of the haze; and
step 7: obtaining a dehazed image based on an atmospheric scattering model, wherein the atmospheric scattering model is as follows:
I(x,y)=J(x,y)t(x,y)+A(x,y)(1−t(x,y)),
where J represents the dehazed image, and t represents transmittance, A represents the final atmospheric light map A (x, y) obtained in the step 6.
Further, in the step 4, Ω(x, y) represents an image block of 9*9.
Further, obtaining the evenly varied atmospheric light map A′(x, y) in the step 5 is as follows:
sorting, from large to small, each row of dark channel values of the rotated dark channel image Idark′(x, y), taking a minimum value of the first 0.1% as the atmospheric light value of the row, and obtaining the atmospheric light value of each row in turn, to obtain an initial atmosphere light map A0(x, y); filtering the initial atmospheric light map, to obtain the evenly varied atmospheric light map A′(x, y).
Further, in the step 5, a mean filtering method is used to filter the initial atmospheric light map.
Further, said obtaining the dehazed image in the step 7 is specifically as follows:
according to a statistical law of dark primary colors, the dark primary colors of the dehazed image J approach 0, that is:
Jdark(x,y)=minC(min(x′,y′)∈Ω(x,y)(JC(x′,y′)))=0,
where Jdark(x, y) represents a dark channel pixel of the dehazed image, Ω(x, y) represents the window of the neighborhood of the point (x, y), and JC(x′, y′) represents a monochrome channel image pixel of the haze image J (x, y);
where A is always positive, then:
minC(min(x′,y′)∈Ω(x,y)(JC(x′,y′)/A(x′,y′)))=0
a rough transmittance diagram is obtained as:
t′(x,y)=1−minC(min(x′,y′)∈Ω(x,y)(IC(x′,y′)/A(x′,y′)));
in clear day, when a distant scene is shielded by little haze, a factor ω is added in order to make a dehazing effect undistorted:
t(x,y)=1−ωminC(min(x′,y′)∈Ω(x,y)(IC(x′,y′)));
solving the dehazed image J using I, t and A and outputting the dehazed image J, where
J(x,y)=(I(x,y)−A(x,y)/t(x,y)+A(x,y).
Further, the factor ω is 0.95.
Compared with the related art, the present disclosure has following beneficial technical effects.
The present disclosure replaces the traditional global atmospheric light value with the atmospheric light map, to make the atmospheric light value of the image be linearly distributed. In the traditional dehaze algorithm, a dark channel algorithm can dehaze most haze images, and the effect is good. However, in a process of dehazing an image in which a depth of field is relatively deep and a haze density of the distant scene is much greater than that of the close area, if a same atmospheric light is used globally to make the effect of the close area of the image good, the brightness in the distant area is too large, which causes a severe distortion, or if the effect in the distance area is good, a brightness value in the close area is too small, which results in loss of details. The present method replaces the original global atmospheric light value with an adaptive linear atmospheric light map, which changes along a direction in which a concentration degree of the haze varies, making the dark channel dehaze algorithm be capable of achieving a good effect even in the dense haze and the area having a relatively large depth of field, so that it has a good dehazing effect, good restoration of the distant scene, and an ideal effect on image processing in the haze weather, and it is of great significance to the further processing of images and accurate acquisition of image information.
The present disclosure will be described in further detail below with reference to the drawings.
Referring to
At step 1, a haze image is obtained in haze weather.
Using an image capture device to obtain a degraded haze image in the haze weather.
At step 2, threshold segmentation is performed on the haze image obtained in the step 1 to obtain a binary image thereof.
The image is first converted into a grayscale image, and then the threshold segmentation is performed on the grayscale image through Otsu algorithm to convert the grayscale image into the binary image.
At step 3, a center of gravity (x0 y0) and an image center (0.5*h 0.5*w) of the binary image (height is h and width is w) in the step 2 are obtained. A normalization is performed by dividing horizontal coordinates and vertical coordinates corresponding to the center of gravity and the image center of the binary image by h and w respectively, so as to obtain a center of gravity (x0′ y0′) and a center (0.5 0.5), where k is defined as a slope and θ is defined as a deflection angle of an atmospheric light value, where
and
θ=arctan(1/k),
in this case, the concentration degree of haze in the image substantially varies and distributes along a direction in which a center line is deflected clockwise by θ.
At step 4, the binary image obtained in the step 2 in which the binary image is subjected to grayscale conversion and binary segmentation is divided into two parts including a bright area and a dark area, and then the center of gravity and the center of the binary image are calculated through the step 3, and a direction in which a line connecting the center of gravity with the center extends is taken as a variation direction of the atmospheric light to tilt the image.
A dark channel of the haze image obtained in the step 1 is obtained according to:
Idark(x,y)=minC∈{r,g,b}(min(x′,y′)∈Ω(x,y)(IC(x′,y′))),
where ω(x, y) represents a window of a neighborhood of a point (x, y), Idark(x, y) represents a dark channel image, and IC(x′, y′) represents a monochrome channel image pixel of the haze image I.
The image dark channel Idark(x, y) is rotated counterclockwise according to the deflection angle θ obtained in the step 3, to obtain the rotated dark channel. Idark′(x, y).
At step 5, a distribution map of the atmospheric light of the rotated dark channel obtained in the step 4 is obtained and the acquisition includes sorting, from large to small, each row of dark channel values of the rotated dark channel image Idark′(x, y), taking a minimum value among first 0.1% of the row of dark channel values as an atmospheric light value of the row, to obtain the atmospheric light value for the row, to obtain an initial atmosphere light map A0(x, y); and filtering the initial atmospheric light map through a mean filtering method to eliminate an abrupt change in each row of the atmospheric light map, to obtain an evenly varied atmospheric light map A′(x, y).
At step 6, the evenly varied atmospheric light map A′(x, y) obtained in the step 5 is rotated counterclockwise by the atmospheric light value deflection angle θ obtained in the step 3, to obtain a final atmospheric light map A(x, y), which is distributed regularly according to a variation direction of the concentration degree of the haze.
At step 7, an atmospheric scattering model commonly used in research of dehaze algorithms is as follows:
I(x,y)=J(x,y)t(x,y)+A(x,y)(1−t(x,y)),
where J represents a dehazed image, and t represents transmittance. A represents the final atmospheric light map A (x, y) obtained in the step 6.
According to a statistical law of dark primary colors, the dark primary colors of the dehazed image J should approach 0, that is:
Jdark(x,y)=minC(min(x′,y′)∈Ω(x,y)(JC(x′,y′)))=0
where Jdark(x, y) represents a dark channel pixel of the dehazed image, Ω(x, y) represents the window of the neighborhood of the point (x, y), and JC(x′, y′) represents a monochrome channel image pixel of a haze image J(x, y).
As A is always positive, this leads to:
minC(min(x′,y′)∈Ω(x,y)(JC(x′,y′)/A(x′,y′)))=0.
A rough transmittance diagram can be obtained:
t′(x,y)=1−minC(min(x′,y′)∈Ω(x,y)(IC(x′,y′/A(x′,y′)))).
In a clear day, when a distant scene is shielded by little haze, a factor ω is further added to the above formula in such a manner that a dehazing effect is undistorted. The ω is generally about 0.95.
t(x,y)=1−ωminC(min(x′,y′)∈Ω(x,y)(IC(x′,y′))).
The I, t and A can be used to solve the dehazed clear image J, and the dehazed clear image J is output.
J(x,y)=(I(x,y)−A(x,y))/t(x,y)+A(x,y).
A processing effect can be more intuitively seen from processing result comparison and a partially enlarged diagram of
Table 1 is a comparison table of effect parameters by using different algorithms to process the haze image. It can be seen from Table 1 that after the image is dehazed by the improved dark channel dehazing method of the present disclosure, ambiguity, average gradient, contrast, and information entropy are all improved. Thus, it can be seen that the present disclosure further improves the processing effect for the haze image and has better result, so that it is superior to the traditional dark channel dehazing method and has important significance for further research on image dehazing, haze image information extraction and the like.
According to the description above, the improved dark channel image dehazing algorithm of the present disclosure has a good dehazing effect, good restoration of the distant scene, and an ideal effect on processing of images captured in the haze weather, and it is of great significance to the further processing of images and accurate acquisition of image information.
| Number | Date | Country | Kind |
|---|---|---|---|
| 201810387076.2 | Apr 2018 | CN | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/CN2018/125153 | 12/29/2018 | WO | 00 |
| Publishing Document | Publishing Date | Country | Kind |
|---|---|---|---|
| WO2019/205707 | 10/31/2019 | WO | A |
| Number | Name | Date | Kind |
|---|---|---|---|
| 9288458 | Chen | Mar 2016 | B1 |
| 20130050472 | Omer | Feb 2013 | A1 |
| 20140314332 | Mudge | Oct 2014 | A1 |
| 20160005152 | Yang et al. | Jan 2016 | A1 |
| 20170178297 | Fattal | Jun 2017 | A1 |
| 20180122051 | Li | May 2018 | A1 |
| 20190089869 | Fleizach | Mar 2019 | A1 |
| 20190287219 | Guo | Sep 2019 | A1 |
| Number | Date | Country |
|---|---|---|
| 105654440 | Jun 2016 | CN |
| 106548461 | Mar 2017 | CN |
| 107451962 | Dec 2017 | CN |
| 108765309 | Nov 2018 | CN |
| Entry |
|---|
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| Chen et al. (“Haze Removal Using Radial Basis Function Networks for Visibility Restoration Applications,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, Issue: 8; Aug. 2018) (Year: 2018). |
| Yu et al. (“Ensemble Dehazing Networks for Non-homogeneous Haze,” IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops; Date of Conference: Jun. 14-19, 2020) (Year: 2020). |
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| International Search Report (PCT/CN2018/125153); dated Apr. 8, 2019. |
| Number | Date | Country | |
|---|---|---|---|
| 20210049744 A1 | Feb 2021 | US |
