RSMamba: Biologically Plausible Retinex-Based Mamba for Remote Sensing Shadow Removal
Kaichen Chi, Sai Guo, Jun Chu, Qiang Li, Qi Wang
Abstract
Shadow removal is an essential task for remote sensing imagery analysis, which is tricky due to spatial irregular and inhomogeneous degradation distribution. Unfortunately, current shadow removal pipelines face challenges with suboptimal performance and insufficient interpretability. To this end, we unleash the long-sequence modeling potential of State Space Models (SSMs) in the context of shadow removal. Coupled with the accurate perception of traditional Retinex decomposition towards illumination, the well-designed RSMamba enjoys the best of both worlds between superior competitiveness and theoretical intuitiveness. Specifically, RSMamba mimics the retina and cerebral cortex to explore illumination and reflectance. The former drives the selective scan mechanism to enhance the response towards contamination, while the latter serves as a tool to preserve illumination fidelity. In addition, contour and gradient regularizations of illumination and reflectance components reflect the spatial opponency of shadows, which are consistent with the center-surround opponent receptive field of the human visual system. Such a manner incorporates the domain knowledge of neurophysiological mechanisms into neural networks, providing new insights into shadow removal. Extensive experiments demonstrate that RSMamba outperforms state-of-the-art methods.