Optimization and Implementation of Image Defogging Algorithm Based on Field Programmable Gate Array

Images collected under severe weather conditions have problems such as poor contrast and reduced clarity. The deterioration of image quality limits the accuracy of computer vision and the efficiency of automated tasks. This article proposes an image dehazing algorithm based on contrast limited adaptive histogram equalization (CLAHE) and improved multi-scale retinex (MSR). In this algorithm, the input foggy degraded image is first processed by the contrast limited adaptive histogram equalization (CLAHE) algorithm and then the MSR algorithm. When processing the image with the MSR algorithm, the Gamma correction factor is introduced to estimate the incident light, and the surround function in the algorithm is optimized. The results show that compared with the original image, the image processed by this algorithm has improved the information entropy, average gradient, and standard deviation of the image. The hardware circuit was designed and the video real-time dehazing was successfully demonstrated on the field programmable gate array (FPGA), which improved the quality of the video image. A digital analysis of board-level resources and function consumption was conducted, proving that the hardware system in this article belongs to the low-power category.