SSM-Net: Enhancing Compressed Sensing Image Reconstruction with Mamba Architecture and Fast Iterative Shrinking Threshold Algorithm Optimization.

Compressed sensing (CS) is a powerful technique that can reduce data size while maintaining high reconstruction quality, which makes it particularly valuable in high-dimensional image applications. However, many existing methods have difficulty balancing reconstruction accuracy, computational efficiency, and fast convergence. To address these challenges, this paper proposes SSM-Net, a novel framework that combines the state-space modeling (SSM) of the Mamba architecture with the fast iterative shrinking threshold algorithm (FISTA). The Mamba-based SSM module can effectively capture local and global dependencies with linear computational complexity and significantly reduces the computation time compared to Transformer-based methods. In addition, the momentum update inspired by FISTA improves the convergence speed during deep iterative reconstruction. SSM-Net features a lightweight sampling module for efficient data compression, an initial reconstruction module for fast approximation, and a deep reconstruction module for iterative refinement. Extensive experiments on various benchmark datasets show that SSM-Net achieves state-of-the-art reconstruction performance while reducing both training and inference reconstruction time, making SSM-Net a scalable and practical solution for real-time applications of compressed sensing.