Self-Adaptation Feature Attention Network with Multi-Step Fusion for Single Image Dehazing

Abstract

Although single image dehazing has been widely studied as a common low-level computer vision task, it still faces serious challenges such as limited ability to dehaze real foggy pictures. We propose an efficient end-to-end self-adaptation feature attention (SAFA) network with multi-step fusion for this purpose. The proposed SAFA module can adaptively expand the receptive field to obtain the key structure information in space and extract more comprehensive and accurate features. In addition, considering the lack of connection between features acquired at low and high levels in the network, we also implement a multi-step fusion module, which makes the features of different layers in the network complementary effectively in the process of image recovery. The network structure is simplified, and the required computing resources are significantly reduced by decreasing network parameters. For multiple datasets and photographs with real haze, our method demonstrates better efficiency and availability, both quantitatively and qualitatively.