The Research On Sharpening Method Of Foggy Image Based On Hierarchical Hierarchy

Along with the rapid development of China's modernization,image processing equipment such as video surveillance,target tracking,intelligent transportation,and remote sensing have higher and higher requirements for image clarity.However,under severe conditions(such as fog,clouds,etc.),due to these external factors,the acquired images usually have color distortion,unclear or even blurred features.However,the current image sharpening method,whether based on image enhancement technology or based on physical restoration technology,cannot perfect the fog and noise problems contained in the image,and even expands or introduces noise during processing,resulting in recovery.The visual effects of the image are affected.Therefore,it is of great practical significance to study the method of clearing foggy images.In view of the above problems,this paper firstly defogged the foggy image,and achieved image enhancement and image defogging by using the dynamic adaptive contrast contrast histogram equalization algorithm and the optimized contrast model algorithm to obtain a fog-free image.Then,the image is denoised,and the image is subjected to redundant noise removal operation by using wavelet detection based on edge detection and guided filtering algorithm to obtain the final clear image.Therefore,the work of this paper has the following aspects:(1)In view of dealing with the fog image based on the adaptive contrast equalization algorithm,and it is easy to cause distortion phenomenon such as “over-brightness” or color shift of restored image.This paper proposes a dynamic adaptive limit contrast histogram equalization algorithm to adaptively enhance the image.Reduce the color block phenomenon and achieve contrast enhancement of the image.The experimental model is compared with the experimental model.It is verified that the algorithm can enhance the image contrast more friendlyly,remove some fog and complete rough defogging.(2)The fog of most backgrounds or details in the image is not eliminated,and the optimized contrast physical model dehazing algorithm is further introduced to calculate the atmospheric light value and transmittance,and the relationship between contrast and degradation factors is used to make the image reasonable.Refine the defogging to get more color and detail information.By numerical analysis of the images before and after,it shows that the quality of the image is significantly improved after the secondary model is defogged.(3)Since the process of enhancement and defogging is easy to expand the original noise or introduce noise problem,the image is noise-processed by the edge-preserving algorithm to reduce the blurring degree of the image.In this paper,the Kany operator-based guided filtering and morphological-based wavelet denoising algorithm are proposed to denoise the image separately and compare the advantages and disadvantages of the algorithm.