  To acquire a snapshot spectral image, coded aperture snapshot spectral
imaging (CASSI) is proposed. A core problem of the CASSI system is to recover
the reliable and fine underlying 3D spectral cube from the 2D measurement. By
alternately solving a data subproblem and a prior subproblem, deep unfolding
methods achieve good performance. However, in the data subproblem, the used
sensing matrix is ill-suited for the real degradation process due to the device
errors caused by phase aberration, distortion; in the prior subproblem, it is
important to design a suitable model to jointly exploit both spatial and
spectral priors. In this paper, we propose a Residual Degradation Learning
Unfolding Framework (RDLUF), which bridges the gap between the sensing matrix
and the degradation process. Moreover, a Mix$S^2$ Transformer is designed via
mixing priors across spectral and spatial to strengthen the spectral-spatial
representation capability. Finally, plugging the Mix$S^2$ Transformer into the
RDLUF leads to an end-to-end trainable neural network RDLUF-Mix$S^2$.
Experimental results establish the superior performance of the proposed method
over existing ones.
