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

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 math-ematical 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.(c) 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement