Abstract: The traditional defogging algorithm based on physical texture feature cannot remove edge fog effectively,and the performance is bad especially in thick fog．An improved image defogging algorithm was proposed based on constrained evolutionary time frequency weighted filtering．The block processing method was taken,and 3×3 blocks were used to solve the dark color.The dark color was used as the new feature．The constraint evolution condition was set.The smoothing pseudo and RSPWVD-Hough time-frequency analysis were implemented．The RSPWVD-Hough time-frequency feature was extracted as the weighting vector to direct image box filter,and the defogging process was rea-lized．The constrained evolutionary algorithm was used to optimize the result．Because the RSPWVD-Hough time-frequency feature can effectively reflect the edge atomization feature points of the image,the edge defogging performance is perfect．Simulation results show that the improved defogging algorithm can optimize the image defogging performance and improve the operating speed．The penetrating rate is improved greatly．It has good application value in image target recognition on thick fog condition.

Key words: Fogging image,Time-frequency analysis,Filter,Constrained evolution