Huang Chan, Liu Huanwen, Zhang Hanyuan, Wu Su, Jiang Xiaoyun, Fang Yuwei, Zhou Leiming, Hu Jigang
Opt Express. 2024 Jun 3;32(12):20915-20930. doi: 10.1364/OE.518509.
Channeled spectropolarimetry enables real-time measurement of the polarimetric spectral information of the target. A crucial aspect of this technology is the accurate reconstruction of Stokes parameters spectra from the modulated spectra obtained through snapshot measurements. In this paper, a learnable sparse dictionary compressed sensing method is proposed for channeled spectropolarimeter (CSP) spectral reconstruction. Grounded in the compressive sensing framework, this method defines a variable sparse dictionary. It can learn prior knowledge from the measured modulated spectra, continuously optimizing its own structure and parameters iteratively by removing redundant basis functions and refining the matched basis functions. The learned sparse dictionary, post-training, can provide a more accurate sparse representation of the Stokes parameters spectra, enabling the proposed method to achieve more precise reconstruction results. To assess the efficacy of the proposed method, simulations and experiments were conducted, both of which consistently demonstrated the superior performance of the proposed approach. The suggested method is well-positioned to enhance the efficiency and accuracy of polarimetric spectral information retrieval in CSP applications.
通道分光偏振测量法能够实时测量目标的偏振光谱信息。这项技术的一个关键方面是从通过快照测量获得的调制光谱中准确重建斯托克斯参量光谱。本文提出了一种适用于通道分光偏振计（CSP）光谱重建的可学习稀疏字典压缩感知方法。基于压缩感知框架，该方法定义了一个可变稀疏字典。它可以从测量的调制光谱中学习先验知识，通过去除冗余基函数和优化匹配基函数来迭代地持续优化自身结构和参数。经过训练后的学习到的稀疏字典能够为斯托克斯参量光谱提供更准确的稀疏表示，使所提出的方法能够获得更精确的重建结果。为了评估所提方法的有效性，进行了仿真和实验，二者均一致证明了所提方法的卓越性能。所建议的方法有望提高CSP应用中偏振光谱信息检索的效率和准确性。
