VDMamba: Vector Decomposition in Vision Mamba for Image Deraining and Beyond

Recent research employs Mamba for image restoration, yet the intrinsic coupling between deraining characteristics and Mamba architectures remains underexplored. We propose VDMamba, a vector decomposition-based vision Mamba approach that leverages 1D sequential representations to characterize direction-aware rain distributions in the frequency embedding space. The core innovation is the Mamba-based Vector Decomposition and Synthesis Module (VDSM). VDSM derives vertical and horizontal 1D vectors from frequency components and utilizes single-direction Mamba scanning to eliminate direction-specific perturbations. This enables the exploration of global relationships for accurate learning without complex scanning designs. Additionally, these components are encoded via bidirectional coupling for refinement. Experiments on various tasks, including deraining, dehazing, and low-light enhancement, demonstrate VDMamba’s competitive performance. Specifically, it achieves a 0.58 dB PSNR improvement in deraining compared to the NeRD method, while reducing model parameters by 94.3%, computational cost by 88.3%, and inference time by 77.5%.