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In coded aperture snapshot spectral compressive imaging (CASSI) systems,hyperspectral image (HSI) reconstruction methods are employed to recover thespatial-spectral signal from a compressed measurement. Among these algorithms,deep unfolding methods demonstrate promising performance but suffer from twoissues. Firstly, they do not estimate the degradation patterns andill-posedness degree from the highly related CASSI to guide the iterativelearning. Secondly, they are mainly CNN-based, showing limitations in capturinglong-range dependencies. In this paper, we propose a principledDegradation-Aware Unfolding Framework (DAUF) that estimates parameters from thecompressed image and physical mask, and then uses these parameters to controleach iteration. Moreover, we customize a novel Half-Shuffle Transformer (HST)that simultaneously captures local contents and non-local dependencies. Byplugging HST into DAUF, we establish the first Transformer-based deep unfoldingmethod, Degradation-Aware Unfolding Half-Shuffle Transformer (DAUHST), for HSIreconstruction. Experiments show that DAUHST significantly surpassesstate-of-the-art methods while requiring cheaper computational and memorycosts. Code and models will be released athttps://github.com/caiyuanhao1998/MST

Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging