Dual camera snapshot high-resolution-hyperspectral imaging system with parallel joint optimization via physics-informed learning.

Abstract

The hardware architecture of the coded aperture snapshot spectral imaging (CASSI) system is based on a coded mask design, resulting in a poor spatial resolution of the system. Therefore, we consider the use of a physical model of optical imaging and a jointly optimized mathematical model to design a self-supervised framework to solve the high-resolution-hyperspectral imaging problem. In this paper, we design a parallel joint optimization architecture based on a two-camera system. This framework combines the physical model of optical system and a joint optimization mathematical model, which takes full advantage of the spatial detail information provided by the color camera. The system has a strong online self-learning capability for high-resolution-hyperspectral image reconstruction, and gets rid of the dependence of supervised learning neural network methods on training data sets.