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
this https URL

本文提出一个基于 Transformer 网络的 HSI 重建方法，称作 DAUHST。该方法使用 DAUF 框架估算压缩图像和物理掩模的参数，并使用这些参数来控制每一次迭代。同时，自定义的半洗牌 Transformer（HST）可以同时捕获局部内容和非局部依赖性，实验证明 DAUHST 明显优于其他最先进的方法，并具有较低的计算和存储成本。