Video dehazing based on CNN
Xing Zhao, Ting Zhang, Xiang Zhan, Wenxin Chen · 2020
The appearance of outdoor images is easily affected by natural phenomena such as fog and dust, which reduces contrast and color distortion. Video dehazing has a wide range of real-time applications, but the challenges mainly come from large amount of computation and bad real-time performance. In this paper, we propose a video dehazing system which is an end-to-end network based on CNN (Convolutional Neural Network). The dehazing algorithm learns the scene transmission and the global atmospheric light simultaneously, which simplifies the dehaze process and improves the real-time performance. Finally, we process videos through combining the end-to-end dehaze network and bicubic interpolation algorithm, and obtain satisfactory results. The experiment results demonstrate that the proposed method performs favorably against the state-of-the-art methods on both quantitative and qualitative evaluation.