In order to solve the problem of image defogging and enhancement in haze weather, this paper proposes a defogging and enhancement algorithm that combines image layering and dark channels. The algorithm first establishes a dark channel model for the input image, estimates the atmospheric light value and transmittance, and restores the image by defogging. Next, the image is subjected to bilateral filtering and transformation to stretch the gray-level area of the pixels in the low-frequency image information. Or compress, normalize the high-frequency image information, then use the normalized histogram and nonlinear S-curve to perform gray-scale transformation, and finally use the weighted fusion method to effectively merge the low-frequency and high-frequency image information to obtain Output image. Experimental results show that the average gradient and information entropy of the algorithm in the three sets of images are 0.0734 and 7.1733, respectively, which are better than the other three algorithms, and the average contrast and time consumption of the algorithm are 422.6 and 0.76, respectively. It is feasible.