Progressive guidance dehazing network
Yinhu zhao, Xiaofen Xing · 2021
Convolutional neural networks have recently demonstrated high-quality reconstruction of single image dehazing. However, existing methods seldom consider the relationship of haze concentration and image depth. In this paper, we propose an end-to-end single image dehazing network called Progressive Guidance Dehazing Network (PGDN), which gradually recovers the clear image from the shallow to deep areas of its hazy image. Our network consists of progressive dehazing blocks, each of which is followed by a guided filtering layer to reinforce the result. Additionally, deep supervisions are added before and after each guided filtering layer, and the supervisions before the guided filtering layers guarantee that the dehazing blocks further incorporate the mutual content information. Experiments on both synthetic and real-world dataset show that our network achieves superior performance over existing methods in quantitative and qualitative evaluations.