Color Image Dehazing Based On Dark Primary Color Prior And Variational Model

With the development of computer technology,computer vision system has been widely used in many fields of science,engineering,military and so on.However,in bad weather conditions such as fog and haze,the visual quality of the acquired images is often poor,which will adversely affect many outdoor computer vision applications.Therefore,image defogging is very important and is an important content in the field of computer vision.Aiming at the deficiency of the dark prior theory in the process of image defogging,such as blurred edges,loss of texture details,and expansion of image noise,this paper proposes two different image defogging algorithms:(1)in view of the fact that the variational model can effectively remove the image noise and maintain the image edge,a new variational defogging algorithm(H-TVBH algorithm)is proposed by combining dark prior theory and TVBH(Total Variation-Bounded Hessian)model.The split Bregman algorithm is designed to improve the efficiency of the algorithm.(2)Aiming at the problem of losing some texture details,an image defogging algorithm(H-OSV algorithm,H-VO algorithm)based on dark prior and OSV(Osher-Sole-Vese),VO(Vese-Osher)image decomposition model is proposed.The original image is decomposed by the OSV,VO decomposition model to get the structure part and texture part of the image.The structure part is defogged by using the dark prior theory algorithm,and the texture layer information is retained.Finally,the defogging structure layer and texture layer are fused to get the final defogging image.This method makes the defogging image improve the image contrast and enhances the texture details of the image to the maximum.In order to verify the effectiveness of the two algorithm proposed in this paper,a large number of experiments are carried out on the basis of LIVE Image Defogging database,and several images are selected for analysis,and the image defogging effect is analyzed and evaluated from both subjective and objective aspects.By comparing the objective evaluation indexes of PSNR,structural similarity,fog sensing density and contrast enhancement of newly added visible edges,it is proved that the proposed method has higher PSNR,structural similarity,ratio of newly added visible edges and ratio of normalized gradient of visible edges,lower fog sensing density and percentage of saturated black or white pixels.Therefore,the algorithm has good effect on defogging.