Self-filtering image dehazing with self-supporting module

Abstract As a pre-processing step of computer vision applications, single image dehazing remains challenging due to existing inefficiencies in the restoration of content and details. In this paper, the self-supporting dehazing network (SSDN) is proposed to overcome these two problems. For the restoration of image content, the self-filtering block is introduced to remove redundant features, hence improving the representation abilities of learned features. For the recovery of image details, a novel self-supporting module is proposed as a crucial component of the proposed SSDN. With this module, the complementary information among support images that are transformed from multi-level features is explored. By incorporating such information, the self-supporting module can learn more intrinsic image characteristics and generate fine-detail images. Experimental results demonstrate that the proposed SSDN outperforms state-of-the-art dehazing methods in terms of both quantitative accuracy and qualitative visual effect.