Rep-Mamba: Re-Parameterization in Vision Mamba for Lightweight Remote Sensing Image Super-Resolution

The selective space model (Mamba) has recently demonstrated great potential in remote sensing image super-resolution (RSISR) tasks due to its capability for long-range dependency modeling with linear computational complexity. Despite these merits, existing Mamba architectures face two critical challenges in large-scale remote sensing scenarios: 1) neglecting the local semantic integrity due to the unfolding 1-D sequential representations and 2) facing the dilemma between effectiveness and efficiency. To address these issues, we propose Rep-Mamba, a lightweight progressive multiscale feature fusion architecture based on the state-space model (SSM) for RSISR. Specifically, we innovatively design a cross-scale state propagation (CSSP) mechanism and construct a lightweight progressive fusion module (LPFM) to dynamically capture hierarchical spatial dependencies in remote sensing scenes while maintaining high computational efficiency. Moreover, to achieve synergistic optimization between local semantic structure preservation and global context modeling, we introduce differentiable re-parameterization convolution (RepConv), which significantly enhances reconstruction accuracy and visual quality without compromising computational efficiency. Extensive experiments across multiple benchmarks demonstrate that Rep-Mamba achieves a superior tradeoff between accuracy and complexity, highlighting its effectiveness and scalability. The code is available at https://github.com/meigeni0929/Rep-Mambahttps://github.com/meigeni0929/Rep-Mamba