A Refinement Boosted and Attention Guided Deep FISTA Reconstruction Framework for Compressive Spectral Imaging

Hyperspectral images (HSIs) contain rich spatial and spectral information. A double dispersers coded aperture snapshot spectral imaging (DD-CASSI) system takes advantage of compressive sensing (CS) theory to map 3D HSI data into a single 2D measurement. One of key components of DD-CASSI is to reconstruct high quality hyperspectral image from measurement. Traditional model-based methods use mathematical optimization to reconstruct hyperspectral images according to prior knowledge. Current deep learning based methods achieve pleasant results. But fully learned deep learning methods lack interpretability, and model-based deep learning methods cannot achieve pleasant performance. In this paper, we propose a novel HSI reconstruction framework named Refinement Boosted and Attention Guided Tensor FISTA(Fast Iterative Shrinkage-Thresholding Algorithm)-Net (ReAttFISTA-Net), which combines model-based deep learning and fully learned deep learning reconstruction strategies. In this framework, we introduces Attention Guided Fusion Mechanism which enhances spatial-spectral information, refinement sub-network and auxiliary loss terms to improve the reconstruction performance. Extensive experimental results show that the proposed reconstruction algorithm outperforms the state-of-the-art algorithms on both simulation and real-world datasets.