2D-Slice and 3D-Cube Mamba Network for Snapshot Spectral Compressive Imaging

Hyperspectral image (HSI) reconstruction algorithms are fundamental to coded aperture snapshot spectral imaging (CASSI) systems. Recently, deep unfolding networks (DUNs) have emerged as a dominant solution, seamlessly combining traditional optimization frameworks with the strengths of deep learning. Among these, Mamba stands out as a prominent method for modeling long-range dependencies. However, its reliance on one-dimensional (1D) spatial scanning often compromises spectral consistency and spatial coherence, leading to misalignment of neighboring pixels within sequences. To address these limitations, we propose a novel multi-view framework based on 2D-slice modeling, which ensures spatial-spectral continuity in 1D sequences while maintaining computational efficiency. Furthermore, motivated by the need for precise local patch modeling in 2D images, we develop a 3D-cube Mamba model for HSI reconstruction. By integrating the UNet architecture, this model enhances spatial and spectral detail representation through multi-scale receptive field modeling, using fixed cube sizes to dynamically adjust pixel distances. These advancements are incorporated into the A-HQS-accelerated deep unfolding framework, synergistically combining the strengths of 2D-slice and 3D-cube MambaNet to achieve state-of-the-art HSI reconstruction performance. Experimental evaluations on simulated and real-world CASSI datasets demonstrate the efficacy of the proposed approach, achieving superior spectral fidelity and detailed feature representation. The source code is available at: https://github.com/fengyuchao97/SCM-DUN.