3DMAUNet: A 3D Memory-Augmented Deep Unfolding Network with Dynamic Momentum Updating for Snapshot Compressive Spectral Imaging

Coded aperture snapshot spectral compressive imaging (CASSI) enables the recovery of hyperspectral images (HSIs) from a single two-dimensional compressive measurement, serving as a core paradigm in modern computational imaging. However, HSI reconstruction from under-sampled measurements is a severely ill-posed inverse problem. Existing deep unfolding methods typically treat each iterative stage independently and model spatial–spectral features in a decoupled manner, which neglects intrinsic spatial–spectral coupling and lacks persistent cross-stage information propagation. To tackle these issues, we propose a deep dynamic 3D memory-augmented unfolding network (3DMAUNet) for HSI reconstruction. Based on the momentum-accelerated generalized alternating projection framework, 3DMAUNet retains the strict mathematical interpretability of iterative optimization while integrating data-driven learnable components. Specifically, we design two key modules: (1) a 3D memory-augmented learning module equipped with a persistent memory bank, which explicitly encodes cross-layer spatial–spectral correlations to enable efficient inter-stage information transfer and long-range dependency modeling; and (2) a learnable dynamic fast momentum updating module, which fuses cross-stage spatial–spectral attention features to estimate task-adaptive momentum coefficients, accelerating convergence and enhancing the robustness of feature updating. Comprehensive experiments on synthetic and real-world HSI datasets demonstrate that 3DMAUNet outperforms state-of-the-art methods in both quantitative and qualitative reconstruction performance.