Deeply learned broadband encoding stochastic hyperspectral imaging

• 作者: Wenyi Zhang Hongya Song Xin He Longqian Huang Xiyue Zhang Junyan Zheng Weidong Shen Xiang Hao Xu Liu
• 来源期刊: 光：科学与应用（英文版）
• 年，卷（期）: 2021,（6）
• 所属期刊栏目: Letters
• 页码范围: 969-975
• 页数: 7页
• 语种: 英文

摘要

Many applications requiring both spectral and spatial information at high resolution benefit from spectral imaging. Although different technical methods have been developed and commercially available, computational spectral cameras represent a compact, lightweight, and inexpensive solution. However, the tradeoff between spatial and spectral resolutions, dominated by the limited data volume and environmental noise, limits the potential of these cameras. In this study, we developed a deeply learned broadband encoding stochastic hyperspectral camera. In particular, using advanced artificial intelligence in filter design and spectrum reconstruction, we achieved 7000–11,000 times faster signal processing and～10 times improvement regarding noise tolerance. These improvements enabled us to precisely and dynamically reconstruct the spectra of the entire field of view, previously unreachable with compact computational spectral cameras.