Single fog image restoration via multi-scale image fusion
Yin Gao, Yijing Su, Qiming Li, Jun Li · 2017
The captured images and videos in bad weather usually have degraded quality by reduced contrast and faded colors, and is very difficult to achieve promising performance. The traditional prior techniques are not sufficient to address this challenging problem to deal with the halo artifacts and brightness distortion problems. In this paper, we propose a multi-scale fusion method for single fog image restoration. By creating two divided regions, the global atmospheric light can be effectively obtained in the sky regions. To properly optimize the transmission, our method is designed in a new Kirsch operators with adaptive boundary constraint. With a new multi-scale image fusion method, we can effectively remove and fuse the haze from these images. The proposed method reduces the halo artifacts by adaptively limiting the boundary of an arbitrary haze image. A new multi-scale image fusion method for single image dehazing has also been proposed to produce a more nature visual recovery effect. Experimental results show that this method outperforms state-of-the-art haze removal methods in terms of both efficiency and the dehazing visual effect.