Multi-Scale Large Kernel Attention for Single-Image Deraining

Rain streaks inherently exhibit spatial heterogeneity. Although recent hybrid CNN–Transformer single-image deraining(SID) methods benefit from complementary local and global modeling to recover clear images from the degraded ones. However, these methods consistently suffer from module redundancy and suboptimal feature fusion, due to the co-designed CNN–Transformer architecture and the resulting misalignment in learned representations. In this paper, we propose a Multi-Scale Large-Kernel Attention (MLA) module and a Ghost Kernel Gate (GKG) to jointly model long-range dependencies and local details within a unified convolutional space. Specifically, MLA captures rich multi-scale contextual features, while GKG enables local features selection. The unified-space multi-scale feature extraction and fusion strategy effectively handles spatially-varying rain distribution and reduces model parameters. Extensive experiments demonstrate that our method achieves state-of-the-art performance, yielding consistent improvements in both quantitative metrics and visual quality.