Learning Deep Subspace Projection Prior for Dual-Camera Compressive Hyperspectral Imaging

Abstract

Coded aperture snapshot spectral imaging (CASSI) captures the 3-D hyperspectral images (HSI) in the form of 2-D coded images. The dual-camera compressive hyperspectral imaging (DCCHI) can effectively improve the reconstruction quality by adding a parallel complementary panchromatic camera. Several regularization-based methods have been proposed for dual-camera reconstruction. However, the handcrafted priors of these methods are limited in representing the complex intrinsic structure of HSI. In this letter, we propose to learn deep subspace projection prior for dual-camera compressive reconstruction. We first design a deep subspace projection prior regularized dual-camera compressive reconstruction model and minimize it with alternative optimization. Then, we unfold the optimization process into a network. Specifically, the deep subspace projection prior learning leads to features with low-rank characteristics, which could efficiently exploit the spectral correlation of HSI. The dual-camera compressive reconstruction network is learned in an end-to-end manner. Extensive experiments substantiate the performance and efficiency of other start-of-the-art algorithms.