Unsupervised Spatial-spectral Network Learning for Hyperspectral Compressive Snapshot Reconstruction
This work provides an adaptive and unsupervised solution for hyperspectral image reconstruction, which is crucial for researchers and practitioners working with diverse spectral imaging devices and varying spectral response characteristics.
This paper addresses the challenge of reconstructing hyperspectral images from 2D compressive snapshot measurements, a core issue in hyperspectral compressive imaging. The authors propose an unsupervised spatial-spectral network that leverages a spatial-spectral attention module to capture joint spatial-spectral correlations, achieving superior reconstruction results compared to state-of-the-art methods across multiple datasets.
Hyperspectral compressive imaging takes advantage of compressive sensing theory to achieve coded aperture snapshot measurement without temporal scanning, and the entire three-dimensional spatial-spectral data is captured by a two-dimensional projection during a single integration period. Its core issue is how to reconstruct the underlying hyperspectral image using compressive sensing reconstruction algorithms. Due to the diversity in the spectral response characteristics and wavelength range of different spectral imaging devices, previous works are often inadequate to capture complex spectral variations or lack the adaptive capacity to new hyperspectral imagers. In order to address these issues, we propose an unsupervised spatial-spectral network to reconstruct hyperspectral images only from the compressive snapshot measurement. The proposed network acts as a conditional generative model conditioned on the snapshot measurement, and it exploits the spatial-spectral attention module to capture the joint spatial-spectral correlation of hyperspectral images. The network parameters are optimized to make sure that the network output can closely match the given snapshot measurement according to the imaging model, thus the proposed network can adapt to different imaging settings, which can inherently enhance the applicability of the network. Extensive experiments upon multiple datasets demonstrate that our network can achieve better reconstruction results than the state-of-the-art methods.