Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive ImagingDownload PDF

Published: 31 Oct 2022, Last Modified: 06 Oct 2022NeurIPS 2022 AcceptReaders: Everyone
Keywords: Applications, Computer Vision, Low-level Vision, Image Restoration, Snapshot Compressive Imaging, Hyperspectral Image Reconstruction
TL;DR: The first Transformer-based deep unfolding method for hyperspectral image reconstruction
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 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 surpasses state-of-the-art methods while requiring cheaper computational and memory costs. Code and models are publicly available at
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