Hyperspectral image (HSI) reconstruction aims to recover the 3D
spatial-spectral signal from a 2D measurement in the coded aperture snapshot
spectral imaging (CASSI) system. The HSI representations are highly similar and
correlated across the spectral dimension. Modeling the inter-spectra
interactions is beneficial for HSI reconstruction. However, existing CNN-based
methods show limitations in capturing spectral-wise similarity and long-range
dependencies. Besides, the HSI information is modulated by a coded aperture
(physical mask) in CASSI. Nonetheless, current algorithms have not fully
explored the guidance effect of the mask for HSI restoration. In this paper, we
propose a novel framework, Mask-guided Spectral-wise Transformer (MST), for HSI
reconstruction. Specifically, we present a Spectral-wise Multi-head
Self-Attention (S-MSA) that treats each spectral feature as a token and
calculates self-attention along the spectral dimension. In addition, we
customize a Mask-guided Mechanism (MM) that directs S-MSA to pay attention to
spatial regions with high-fidelity spectral representations. Extensive
experiments show that our MST significantly outperforms state-of-the-art (SOTA)
methods on simulation and real HSI datasets while requiring dramatically
cheaper computational and memory costs. Code and pre-trained models are
available at this https URL

本文提出 Mask-guided Spectral-wise Transformer (MST) 框架，通过 Spectral-wise Multi-head Self-Attention 和 Mask-guided Mechanism 的组合，可以有效解决 CNN-based 方法捕捉波谱相似性以及长距离依赖性上的局限性，而且能够充分利用 CASSI 系统中物理屏的信息对高保真度的 HSI 重建有提升作用。