Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging

TL;DR

This paper introduces a novel Transformer-based deep unfolding framework for spectral compressive imaging that estimates degradation patterns and captures long-range dependencies, leading to superior hyperspectral image reconstruction.

Contribution

It proposes the first Transformer-based deep unfolding method, DAUHST, which estimates degradation parameters and enhances reconstruction performance in spectral imaging.

Findings

DAUHST outperforms state-of-the-art methods in accuracy.

It requires less computational and memory resources.

The framework effectively estimates degradation patterns from measurements.

Abstract

In coded aperture snapshot spectral compressive imaging (CASSI) systems, hyperspectral image (HSI) reconstruction methods are employed to recover the spatial-spectral signal from a compressed measurement. Among these algorithms, deep unfolding methods demonstrate promising performance but suffer from two issues. Firstly, they do not estimate the degradation patterns and ill-posedness degree from the highly related CASSI to guide the iterative learning. Secondly, they are mainly CNN-based, showing limitations in capturing long-range dependencies. In this paper, we propose a principled Degradation-Aware Unfolding Framework (DAUF) that estimates parameters from the compressed image and physical mask, and then uses these parameters to control each iteration. Moreover, we customize a novel Half-Shuffle Transformer (HST) that simultaneously captures local contents and non-local dependencies. By plugging HST into DAUF, we establish the first Transformer-based deep unfolding method, Degradation-Aware Unfolding Half-Shuffle Transformer (DAUHST), for HSI reconstruction. Experiments show that DAUHST significantly surpasses state-of-the-art methods while requiring cheaper computational and memory costs. Code and models will be released at https://github.com/caiyuanhao1998/MST