ADMM-Inspired Reconstruction Network for Compressive Spectral Imaging

Abstract

Compressive spectral imaging (CSI) is an emerging technique for acquiring hyperspectral images based on compressed sensing theory. By combining powerful data-driven methods with traditional iterative convex optimization algorithms, we propose a multi-staged neural network architecture, which is inspired by ADMM, to achieve fast and accurate reconstruction for CSI. All parameters of ADMM and deep hyperspectral priors are jointly learned. The proposed ADMM-inspired network was experimentally shown to outperform an existing state-of-the-art reconstruction method in terms of both speed and accuracy.