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 3-D HSI data into a single 2-D measurement. One of the key components of DD-CASSI is to reconstruct high-quality HSI from measurement. Traditional model-based methods use mathematical optimization to reconstruct HSIs according to prior knowledge. Current deep learning-based methods achieve pleasant results. However, fully learned deep learning methods lack interpretability, and model-based deep learning methods cannot achieve pleasant performance. In this article, we propose a novel HSI reconstruction framework na med refinement boosted and attention guided tensor 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 introduce an attention guided fusion mechanism, which enhances spatial-spectral information, refinement subnetwork, 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.

Automated summary

This article proposes a novel HSI reconstruction framework that combines model-based deep learning and fully learned deep learning reconstruction strategies. The proposed algorithm outperforms state-of-the-art methods on both simulation and real-world datasets.