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To acquire a snapshot spectral image, coded aperture snapshot spectralimaging (CASSI) is proposed. A core problem of the CASSI system is to recoverthe reliable and fine underlying 3D spectral cube from the 2D measurement. Byalternately solving a data subproblem and a prior subproblem, deep unfoldingmethods achieve good performance. However, in the data subproblem, the usedsensing matrix is ill-suited for the real degradation process due to the deviceerrors caused by phase aberration, distortion; in the prior subproblem, it isimportant to design a suitable model to jointly exploit both spatial andspectral priors. In this paper, we propose a Residual Degradation LearningUnfolding Framework (RDLUF), which bridges the gap between the sensing matrixand the degradation process. Moreover, a Mix$S^2$ Transformer is designed viamixing priors across spectral and spatial to strengthen the spectral-spatialrepresentation capability. Finally, plugging the Mix$S^2$ Transformer into theRDLUF leads to an end-to-end trainable neural network RDLUF-Mix$S^2$.Experimental results establish the superior performance of the proposed methodover existing ones.

Residual Degradation Learning Unfolding Framework with Mixing Priors across Spectral and Spatial for Compressive Spectral Imaging