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

TL;DR

This paper introduces a novel unfolding framework with a mixing prior transformer for improved spectral image reconstruction in CASSI systems, addressing device errors and enhancing spectral-spatial feature learning.

Contribution

It proposes the RDLUF framework to better model degradation and introduces the Mix$S^2$ Transformer for joint spectral-spatial priors, leading to an end-to-end trainable network.

Findings

Outperforms existing spectral imaging methods

Effectively models real device degradation

Enhances spectral-spatial feature integration

Abstract

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.