Xu Hao, Chen Shiqi, Hu Haiquan, Luo Peng, Jin Zheyan, Li Qi, Xu Zhihai, Feng Huajun, Chen Yueting, Jiang Tingting
Opt Express. 2024 Mar 25;32(7):10741-10760. doi: 10.1364/OE.506997.
Hyperspectral imaging is a critical tool for gathering spatial-spectral information in various scientific research fields. As a result of improvements in spectral reconstruction algorithms, significant progress has been made in reconstructing hyperspectral images from commonly acquired RGB images. However, due to the limited input, reconstructing spectral information from RGB images is ill-posed. Furthermore, conventional camera color filter arrays (CFA) are designed for human perception and are not optimal for spectral reconstruction. To increase the diversity of wavelength encoding, we propose to place broadband encoding filters in front of the RGB camera. In this condition, the spectral sensitivity of the imaging system is determined by the filters and the camera itself. To achieve an optimal encoding scheme, we use an end-to-end optimization framework to automatically design the filters' transmittance functions and optimize the weights of the spectral reconstruction network. Simulation experiments show that our proposed spectral reconstruction network has excellent spectral mapping capabilities. Additionally, our novel joint wavelength encoding imaging framework is superior to traditional RGB imaging systems. We develop the deeply learned filter and conduct actual shooting experiments. The spectral reconstruction results have an attractive spatial resolution and spectral accuracy.
高光谱成像是在各种科学研究领域中收集空间光谱信息的关键工具。由于光谱重建算法的改进，从常见获取的RGB图像重建高光谱图像已取得显著进展。然而，由于输入有限，从RGB图像重建光谱信息是不适定的。此外，传统相机彩色滤光片阵列（CFA）是为人类感知设计的，对于光谱重建并非最优。为了增加波长编码的多样性，我们建议在RGB相机前放置宽带编码滤光片。在这种情况下，成像系统的光谱灵敏度由滤光片和相机本身决定。为了实现最优编码方案，我们使用端到端优化框架自动设计滤光片的透过率函数并优化光谱重建网络的权重。仿真实验表明，我们提出的光谱重建网络具有出色的光谱映射能力。此外，我们新颖的联合波长编码成像框架优于传统RGB成像系统。我们开发了深度学习滤光片并进行了实际拍摄实验。光谱重建结果具有吸引人的空间分辨率和光谱精度。
