Hyperspectral image reconstruction using a lightweight frequency-enhanced network with spectral-spatial dual priors

Hyperspectral image (HSI) reconstruction based on coded aperture snapshot spectral imaging (CASSI) systems aims to recover 3D HSI data from 2D measurements, which is an ill-posed inverse problem. In this paper, by taking advantage of mask modulation information and the spectral correlation, we propose a lightweight frequency-enhanced network with spectral-spatial dual priors (called FSDPNet). First, we treat the modulation mask as a spatial prior and propose frequency-spatial reconstruction modules (FSRMs) to progressively recover spatial details. In FSRMs, frequency learning blocks are designed to model long-range spatial dependencies in the frequency domain, and enhance the conventional spatial feature learning. Second, based on the spectral prior of HSIs, we design a spectral similarity loss to guide the reconstruction process. Experimental results on both simulation and real HSI datasets demonstrate that FSDPNet with few network parameters (0.98M) outperforms state-of-the-art methods in terms of reconstruction quality.