Multi-Scale Dilated Convolution Transformer for Single Image Deraining
Xianhao Wu, Jiyang Lu, Jindi Wu, Yufeng Li · 2023
Recently, Transformer-based methods have achieved significant improvements over convolutional neural networks (CNNs) in single image deraining, due to the powerful ability of modeling non-local information. In fact, rich local-global information representations are equally important for better satisfying rain removal. In this paper, we propose an effective image deraining method by integrating a CNN model into the Transformer backbone to accelerate network convergence, called Multi-scale Dilated-convolution Transformer (MDT), which fully leverages the learning capabilities of Transformers on non-local features, seamlessly integrating local detail extraction and global structural representation. The fundamental building unit of our framework is the Multi-scale Dilated-convolution Transformer Block (MDTB) with different dilation rates, which consists of the Dilconv Self-Attention (DSA) and the Dilconv Feed-Forward Network (DFN). Specifically, the former processes the contextual information via dilated convolutions and enables the model to emphasize spatially-varying rain distribution features, while the latter integrates the dual-branch information to facilitate the local feature learning for better feature aggregation. Extensive evaluations demonstrate that our model reaches superior performance, significantly improving the image deraining quality.