Opt Express. 2023 Apr 24;31(9):14617-14639. doi: 10.1364/OE.487253.
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.
编码孔径快照光谱成像（CASSI）系统的硬件架构基于编码掩模设计，导致系统的空间分辨率较差。因此，我们考虑使用光学成像的物理模型和联合优化的数学模型来设计一个自监督框架，以解决高分辨率高光谱成像问题。在本文中，我们设计了一种基于双相机系统的并行联合优化架构。该框架结合了光学系统的物理模型和联合优化数学模型，充分利用了彩色相机提供的空间细节信息。该系统对高分辨率高光谱图像重建具有强大的在线自学习能力，摆脱了监督学习神经网络方法对训练数据集的依赖。
