A Hybrid Transformer-Mamba Network for Single Image Deraining

Understand

Existing deraining Transformers employ self-attention mechanisms with fixed-range windows or along channel dimensions, limiting the exploitation of non-local receptive fields.

• In response to this issue, we introduce a novel dual-branch hybrid Transformer-Mamba network, denoted as TransMamba, aimed at effectively capturing long-range rain-related dependencies.
• Based on the prior of distinct spectral-domain features of rain degradation and background, we design a spectral-banded Transformer blocks on the first branch.
• Self-attention is executed within the combination of the spectral-domain channel dimension to improve the ability of modeling long-range dependencies.