MS-RainMamba: Learning Multi-Scale State Space Models for Single Image Deraining

Abstract

Despite the significant advances of Convolutional neural networks (CNNs) and Transformers in image deraining, they either suffer from limited receptive fields or incur quadratic complexity, leading to an imbalance between performance and efficiency. Recently, state space models (SSMs) have demonstrated significant potential in modeling long-range dependencies while maintaining linear complexity. However, existing Mamba-based approaches lack the exploration of useful complementary information from multiple image scales, which could be beneficial for facilitating rain removal. In this paper, we propose an effective multi-scale state-space model-based framework (MS-RainMamba) to explore richer scale-space information for better image deraining. Specifically, we design a local-enhanced state space module to better aggregate rich local and global information. In contrast to existing methods that adopt fixed-scale scanning for feature extraction, we develop a multi-scale hierarchical 2D scanning technique to better help image restoration. Experimental results on six benchmarks show that the proposed method performs favorably against state-of-the-art models.