Multi-Scale Fusion of Gated Neighborhood Attention Transformers for Single Image Deraining

Since the diverse geometric appearances and densities of rain streaks, local-global information is equally essential for single image deraining. Balancing local-global information becomes a challenge. Thus, a Multi-Scale Fusion of Gated Neighborhood Attention Transformers (MSF-GNAT) for single image deraining is proposed in this paper. Firstly, a Gated Neighborhood Attention Transformer (GNAT) block is designed to achieve complex condition deraining by complementarily fusing global and local information. Secondly, a Multi-Scale Fusion (MSF) block is developed to fuse multi-scale information for richer representation. Moreover, a Simplified Gated Feed-forward Network (SGFN) is proposed to ensure that the convolutional results primarily attend to valid pixels. Evaluation experiments on synthetic and real-world datasets demonstrate the superiority of our MSF-GNAT over state-of-the-art methods. The source code is available at https://github.com/SWU-CS-MediaLab/MSF-GNAT.