ShadowMamba: State-Space Model with Boundary-Region Selective Scan for Shadow Removal

Image shadow removal is a typical low-level vision task. Shadows cause local brightness shifts, which reduce the performance of downstream vision tasks. Currently, Transformer-based shadow removal methods suffer from quadratic computational complexity due to the self-attention mechanism. To improve efficiency, many approaches use local attention, but this limits the ability to model global information and weakens the perception of brightness changes between regions. Recently, Mamba has shown strong performance in vision tasks by enabling global modeling with linear complexity. However, existing scanning strategies are not suitable for shadow removal, as they ignore the semantic continuity of shadow boundaries and internal regions. To address this, this paper proposes a boundary-region selective scanning mechanism that captures local details while enhancing semantic continuity between them, effectively improving shadow removal performance. In addition, a shadow mask denoising method is introduced to support the scanning mechanism and improve data quality. Based on these techniques, this paper presents a model called ShadowMamba, the first Mamba-based model designed for shadow removal. Experimental results show that the proposed method outperforms existing mainstream approaches on the AISTD, ISTD, and SRD datasets, and also offers clear advantages in parameter efficiency and computational complexity. Code is available at: https://github.com/ZHUXIUJINChris/ShadowMamba