We consider the problem of hyperspectral image (HSI) reconstruction, which aims to recover 3D hyperspectral data from 2D compressive HSI measurements acquired by a coded aperture snapshot spectral imaging (CASSI) system. Existing deep learning methods have achieved acceptable results in HSI reconstruction. However, these methods did not consider the imaging system degradation pattern. In this article, based on observing the initialized HSIs obtained by shifting and splitting the measurements, we propose a dynamic Fourier network based on degradation learning, called the degradation-aware dynamic Fourier-based network (DADF-Net). We estimate the degradation feature maps from the degraded hyperspectral images to realize the linear transformation and dynamic processing of the features. In particular, we use the Fourier transform to extract the HSI non-local features. Extensive experimental results show that the proposed model outperforms state-of-the-art algorithms on simulation and real-world HSI datasets.

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.