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.

翻译：为获取快照式光谱图像，提出了编码孔径快照光谱成像（CASSI）系统。CASSI系统的核心问题是从二维测量值中恢复可靠且精细的三维光谱立方体。通过交替求解数据子问题和先验子问题，深度展开方法取得了良好性能。然而，在数据子问题中，由于相位像差、畸变导致的器件误差，所使用的传感矩阵与实际退化过程不匹配；在先验子问题中，设计合适的模型以联合利用空间和光谱先验至关重要。本文提出了一种残差退化学习展开框架（RDLUF），该框架弥合了传感矩阵与退化过程之间的差距。此外，通过混合光谱与空间先验，设计了Mix$S^2$ Transformer，以增强光谱-空间表示能力。最终，将Mix$S^2$ Transformer嵌入RDLUF中，形成端到端可训练的神经网络RDLUF-Mix$S^2$。实验结果表明，所提方法相较于现有方法具有优越性能。