Selective Attention Network for Image Dehazing and Deraining

Image dehazing and deraining are import low-level compute vision tasks. In this paper, we propose a novel method named Selective Attention Network (SAN) to solve these two problems. Due to the density of haze and directions of rain streaks are complex and non-uniform, SAN adopts the channel-wise attention and spatial-channel attention to remove rain streaks and haze both in globally and locally. To better capture various of rain and hazy details, we propose a Selective Attention Module(SAM) to re-scale the channel-wise attention and spatial-channel attention instead of simple element-wise summation. In addition, we conduct ablation studies to validate the effectiveness of the each module of SAN. Extensive experimental results on synthetic and real-world datasets show that SAN performs favorably against state-of-the-art methods.