Dehazing network based on haze density
Yanling Hua, Zuo Zhengrong · 2020
Single image dehazing is a challenging ill-posed restoration problem. Most of dehazing algorithms follow the classical atmospheric scattering model and adopt same parameters for different hazy density areas in hazy images. In this paper, we proposed an end-to-end dehazing algorithm, called Dehazing Network based on Haze Density(DNBHD). The proposed network involves a haze density map estimation network and a dehazing network. By the estimated haze density map, hazy image is divided into a mist region and a dense fog region which are respectively feed into dehazing network. Compared with previous dehazing algorithm, DNBHD is independent on the atmospheric scattering model, and considers uniform fog distribution in images. We use different parameters to handle different hazy density regions, avoiding color distortion and inappropriate brightness caused by overall defogging. The experiments show our algorithm achieves significant improvements over the state-of-the-art methods