Decoupled Deep Unrolling Model for Snapshot Spectral Imaging

Coded Aperture Snapshot Spectral Imaging (CASSI) compresses a 3D hyperspectral image (HSI) into a 2D measurement through cascaded mask modulation, spectral dispersion, and spectral integration. Existing deep unrolling methods typically represent these operations using a single coupled forward model and solve the resulting highly ill-posed reconstruction problem without dedicated regularization of intermediate physical states. To address these limitations, we propose the Decoupled Deep Unrolling Model (DDUM), which explicitly introduces intermediate state variables along the physical imaging chain of CASSI. Under a Bayesian formulation, DDUM decomposes reconstruction into three physically interpretable subproblems: integration inversion, spectral alignment, and mask demodulation. This decoupling enables dedicated regularization networks to model the distinct priors of different intermediate states, yielding more faithful reconstruction. To address the ambiguity inherent in many-to-one spectral integration, we further develop a learnable generalized physical inverse operator. We further analyze the inverse properties of the coupled and constituent operators. Extensive experiments demonstrate that DDUM consistently outperforms state-of-the-art methods and that adapting existing unrolling networks to our framework further improves their reconstruction performance.