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Poster
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging
Yuanhao Cai · Jing Lin · Haoqian Wang · Xin Yuan · Henghui Ding · Yulun Zhang · Radu Timofte · Luc V Gool

Wed Nov 30 02:00 PM -- 04:00 PM (PST) @ Hall J #141
in Poster Session 4 »

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 https://github.com/caiyuanhao1998/MST

Keywords: Computer Vision · Applications · Image restoration · Hyperspectral Image Reconstruction · Low-level Vision · Snapshot Compressive Imaging
[ Paper]  [ Poster]  [ OpenReview] 

Author Information

Yuanhao Cai (Tsinghua Shenzhen International Graduate School)
Jing Lin
Haoqian Wang (Tsinghua Shenzhen International Graduate School)
Xin Yuan (Westlake University)
Henghui Ding (Swiss Federal Institute of Technology)
Yulun Zhang (ETH Zürich)
Radu Timofte (Bayerische Julius-Maximilians-Universität Würzburg)
Luc V Gool (Computer Vision Lab, ETH Zurich)

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