[Image dehazing] Image dehazing based on color attenuation prior with matlab code

1 Introduction

Haze is the result of the interaction between specific climatic conditions and human activities, and the widely existing suspended particles in the atmosphere in haze weather will inevitably absorb and scatter light to a certain extent, resulting in a significant decrease in the visual effect of outdoor images, manifested as blurred image content, decreased contrast, and color attenuation.Among them, the aerial image vision system is particularly affected by fog and haze weather because the images it collects often have a large depth of field, and even the aerial images obtained under sunny conditions are often disturbed by the accumulated moisture, fog and haze.The comparison of degraded aerial images in haze weather is shown in Figure 1.In view of the fact that many algorithms in computer vision related to image understanding [1], target recognition [2], target tracking [3] and other fields are based on the premise that the input image or video is taken under ideal weather conditions, eliminating hazeIt has significant research significance and application value to restore the corresponding sunny image from the negative effect of the image caused by the weather.In the early days, the processing methods of image dehazing were mainly divided into three categories: (1) foggy image adjustment method based on histogram equalization technology [4]; (2) foggy image enhancement method based on Retinex model [5];The result of the interaction between climatic conditions and human activities, and the widespread presence of suspended particles in the atmosphere in haze weather is bound to have a certain degree of absorption and scattering of light, resulting in a significant decrease in the visual effect of outdoor images, manifested as blurred image content,Contrast drop and color falloff.Among them, the aerial image vision system is particularly affected by fog and haze weather because the images it collects often have a large depth of field, and even the aerial images obtained under sunny conditions are often disturbed by the accumulated moisture, fog and haze.The comparison of degraded aerial images in haze weather is shown in Figure 1.In view of the fact that many algorithms in computer vision related to image understanding [1], target recognition [2], target tracking [3] and other fields are based on the premise that the input image or video is taken under ideal weather conditions, eliminating hazeIt has significant research significance and application value to restore the corresponding sunny image from the negative effect of the image caused by the weather.In the early days, the processing methods of image dehazing were mainly divided into three categories: (1) foggy image adjustment method based on histogram equalization technology [4]; (2) foggy image enhancement method based on Retinex model [5]; (3) based on homomorphic filtering technologyThe foggy image restoration method [6].Although these methods can improve the visual effect of the image through methods such as image contrast adjustment, color correction and detail highlighting, so as to achieve a certain degree of image visual effect improvement.However, since the degradation mechanism of foggy images is ignored, the image information loss or serious distortion is often caused in the enhancement process.

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Aiming at the degradation mechanism of aerial images in haze weather, this paper proposes a dehazing algorithm for aerial images based on color attenuation prior. Firstly, a quantification map of fog and dense fog is constructed, and combined with clustering technology, the foggy images are decomposed intoThen, an improved global atmospheric light estimation method is proposed, which can effectively improve its estimation accuracy by establishing a candidate area. This paper designs a linear aerial image depth structure estimation model, which can efficiently use the depth of field features of aerial images.Obtain the depth map, and then use the guided filter to improve the edge structure of the depth map. According to the obtained global atmospheric light and depth map, the albedo of the scene can be estimated through the atmospheric scattering model, and then the restored haze-free image can be obtained. Experimental resultsThe comparison verifies the efficiency, robustness and effectiveness of the proposed algorithm.

2 Emulation code

close all;clear;clc;

I = imread('C:\Users\Tiger\Desktop\All code and article finishing\Fog Atlas\18.bmp');

I=double(I)

hsvI = rgb2hsv(I);

s = hsvI(:,:,2);

v = hsvI(:,:,3);

maxI=max(max(v-s));

minI=min(min(v-s));

dif=v-s;

d=(dif-minI)*255/(maxI-minI);

r=15;

output = ordfilt2(d, 1, ones(r,r), 'symmetric');

%----- Parameters for Guided Image Filtering -----

gimfiltR = 30;

eps = 10^-3;

%-----------------------------------------------------------

refineDR = guidedfilter(rgb2gray(I),output,gimfiltR,eps);

figure,imshow([output,refineDR],[])

colormap(hot)

3 Run Results

4 References

[1]Li Qian. Research on image dehazing algorithm based on color attenuation prior and FPGA implementation [D]. Xi'an University of Technology.

[2] Chen Dapeng. Research on image dehazing algorithm based on dark channel and color attenuation prior [D]. Shandong University of Science and Technology.

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