An Improved Dark Channel Prior Method for Video Defogging and Its FPGA Implementation
Lin Wang, Zhongqiang Luo, Li Hua Gao · Symmetry · 2025
In fog, rain, snow, haze, and other complex environments, environmental objects photographed by imaging equipment are prone to image blurring, contrast degradation, and other problems. The decline in image quality fails to satisfy the requirements of application scenarios such as video surveillance, satellite reconnaissance, and target tracking. Aiming at the shortcomings of the traditional dark channel prior algorithm in video defogging, this paper proposes a method to improve the guided filtering algorithm to refine the transmittance image and reduce the halo effect in the traditional algorithm. Meanwhile, a gamma correction method is proposed to recover the defogged image and enhance the image details in a low-light environment. The parallel symmetric pipeline design of the FPGA is used to improve the system’s overall stability. The improved dark channel prior algorithm is realized through the hardware–software co-design of ARM and the FPGA. Experiments show that this algorithm improves the Underwater Image Quality Measure (UIQM), Average Gradient (AG), and Information Entropy (IE) of the image, while the system is capable of stably processing video images with a resolution of 1280 × 720 @ 60 fps. By numerically analyzing the power consumption and resource usage at the board level, the power consumption on the FPGA is only 2.242 W, which puts the hardware circuit design in the category of low power consumption.