Multifocal Attention-Based Cross-Scale Network for Image De-raining

Albeit existing deep learning-based image de-raining methods have achieved promising results, most of them only extract single scale features, and neglect the fact that similar rain streaks appear repeatedly across different scales. Therefore, this paper aims to explore the cross-scale cues in a multi-scale fashion. Specifically, we first introduce an adaptive-kernel pyramid to provide effective multi-scale information. Then, we design two cross-scale similarity attention blocks (CSSABs) to search spatial and channel relationships between two scales, respectively. The spatial CSSAB explores the spatial similarity between pixels of cross-scale features, while the channel CSSAB emphasizes the interdependencies among cross-scale features. To further improve the diversity of features, we adopt the wavelet transformation and multi-head mechanism in CSSABs to generate multifocal features which focus on different areas. Finally, based on our CSSABs, we construct an effective multifocal attention-based cross-scale network, which exhaustively utilizes the cross-scale correlations of both rain streaks and background, to achieve image de-raining. Experiments show the superiority of our network over state-of-the-art image de-raining approaches both qualitatively and quantitatively. The source code and pre-trained models are available at https://github.com/zhangzheyu0/Multifocal_derain.