Research On Image De-fogging Based On Fusion Of GF-MSRCR And Dark Channel Prior

Nowadays,there exist some problems such as for the high light region and the thick foggy areas of the images acquired under the fog condition,the inaccurate transmittance calculation often results in detail loss of restored image,halo phenomenon,contrast and color difficult to meet human visual characteristics.Thus,this paper proposed an image defogging algorithm combining GF-MSRCR with dark channel prior.Firstly,weighted quad tree method is adopted to fast search the minimum channel graph to obtain the global atmospheric light value,then GF-MSRCR algorithm is used to preliminarily estimate the transmittance for the image enhancement,and according to the dark channel prior theory the minimum channel graph is estimated again,afterwards the pixels fusion is operated on the above two results with a certain proportion to get the transmittance estimation value,which is further modified by variation function,and next optimized by median filtering to acquire the precise transmittance value,finally,the atmospheric scattering model is used to restore the foggy image,and thereby the haze-removed image with complete contour and clear details is obtained after contrast and color correction.Experimental results show that it should be noted that running time obviously decreased by 53.22% with the increase of the information entropy by 7.87%,contrast by 21.95%,average gradient by 47.73% and structural similarity by 15.58%.The algorithm presented in this paper shows good restoration results for scene images containing fog close shot,a small sky area,a large sky area or white objects.The image dehazing algorithm fusing GF-MSRCR and dark channel prior could quickly and effectively retain image details,eliminate halo and satisfy human visual characteristics,possessing certain practicability and universality.This paper has 24 figures,7 tables and 50 references.