论文标题
残留的降解学习与跨光谱和空间的混合框架一起展开框架，用于压缩光谱成像
Residual Degradation Learning Unfolding Framework with Mixing Priors across Spectral and Spatial for Compressive Spectral Imaging
论文作者
论文摘要
为了获取快照光谱图像，提出了编码的光圈快照光谱成像（CASSI）。 CASSI系统的核心问题是从2D测量中恢复可靠且良好的3D光谱立方体。通过交替求解数据子问题和先前的子问题，深入展开的方法可实现良好的性能。但是，在数据子问题中，由于相位畸变，失真引起的设备误差，使用的传感矩阵不适合实际退化过程。在先前的子问题中，设计合适的模型以共同利用空间和光谱先验很重要。在本文中，我们提出了一个残留的退化学习展开框架（RDLUF），该学习弥合了传感矩阵与降解过程之间的差距。此外，混合$ s^2 $变压器是通过在光谱上混合先验和空间来设计的，以增强光谱空间表示能力。最后，将Mix $ S^2 $变压器插入RDLUF会导致端到端可训练的神经网络RDLUF-MIX $ S^2 $。实验结果确定了所提出方法的优越性能。
To acquire a snapshot spectral image, coded aperture snapshot spectral imaging (CASSI) is proposed. A core problem of the CASSI system is to recover the reliable and fine underlying 3D spectral cube from the 2D measurement. By alternately solving a data subproblem and a prior subproblem, deep unfolding methods achieve good performance. However, in the data subproblem, the used sensing matrix is ill-suited for the real degradation process due to the device errors caused by phase aberration, distortion; in the prior subproblem, it is important to design a suitable model to jointly exploit both spatial and spectral priors. In this paper, we propose a Residual Degradation Learning Unfolding Framework (RDLUF), which bridges the gap between the sensing matrix and the degradation process. Moreover, a Mix$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.
