MAPD: An FPGA-Based Real-Time Video Haze Removal Accelerator Using Mixed Atmosphere Prior

Real-time video dehazing plays a key role in helping autonomous driving detect pedestrians or obstacles in severe foggy weather to prevent potential hazards. Existing video dehazing methods achieve good restoration performance but still suffer from over-saturation and low dehazing speed, especially for HD (high-resolution) videos. In this paper, we propose a mixed atmosphere prior information video dehazing accelerator (MAPD) and implement it on FPGA (Field Programmable Gate Array) to achieve real-time haze removal for HD video. MAPD provides a mixed atmospheric light model by applying heterogeneous atmospheric light in the foreground area to balance brightness deviation, and maintaining the global atmospheric light in the background region. Considering the parallel characteristics of FPGA, MAPD leverages the redundant information between adjacent frames to accelerate the dehazing process and designs an indirect transmission estimation to decrease resource consumption. For comparison, we also implement six dehazing solutions (DCP, CEP, RDCP, FFVD, MHVD, and REFD) on FPGA, and deploy a GPU-based method (D4) on the platform with Nvidia 3080 GPU. Experiments using two widely used benchmarks show that MAPD increases the performance by up to 36.5%, 53.5%, 36.3%, 33.3%, 11.9%, 23.3%, decreases resource consumption by up to 79.7%, 75.0%, 74.8%, 25.6%, 22.6%, 73.9% and enhances FPS for HD videos by up to 241.6%, 145.9%, 151.7%, 68.6%, 50.6%, 62.4%, compared with DCP, CEP, RDCP, FFVD, MHVD, and REFD. Compared to D4, MAPD also promotes the dehazing performance by up to 21.8%, and increases FPS by up to 487.0%.