Chin. Phys. Lett.  2017, Vol. 34 Issue (10): 104203    DOI: 10.1088/0256-307X/34/10/104203
FUNDAMENTAL AREAS OF PHENOMENOLOGY(INCLUDING APPLICATIONS) |
Computational Spectral Imaging Based on Compressive Sensing
Chao Wang1,2,3, Xue-Feng Liu3**, Wen-Kai Yu1, Xu-Ri Yao3, Fu Zheng3, Qian Dong3, Ruo-Ming Lan4, Zhi-Bin Sun3, Guang-Jie Zhai3, Qing Zhao1
1Center for Quantum Technology Research, School of Physics, Beijing Institute of Technology, Beijing 100081
2China Academy of Engineering Physics, Mianyang 621900
3Key Laboratory of Electronics and Information Technology for Space Systems, National Space Science Center, Chinese Academy of Sciences, Beijing 100190
4School of Physics and Electronics, Shandong Normal University, Jinan 250014
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Chao Wang, Xue-Feng Liu, Wen-Kai Yu et al  2017 Chin. Phys. Lett. 34 104203
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Abstract Spectral imaging is an important tool for a wide variety of applications. We present a technique for spectral imaging using computational imaging pattern based on compressive sensing (CS). The spectral and spatial information is simultaneously obtained using a fiber spectrometer and the spatial light modulation without mechanical scanning. The method allows high-speed, stable, and sub sampling acquisition of spectral data from specimens. The relationship between sampling rate and image quality is discussed and two CS algorithms are compared.
Received: 09 August 2017      Published: 27 September 2017
PACS:  42.30.Wb (Image reconstruction; tomography)  
  42.30.Va (Image forming and processing)  
  42.62.Eh (Metrological applications; optical frequency synthesizers for precision spectroscopy)  
  42.79.Hp (Optical processors, correlators, and modulators)  
Fund: Supported by the National Major Scientific Instruments Development Project of China under Grant No 2013YQ030595, the National Natural Science Foundation of China under Grant Nos 11675014, 61601442, 61605218, 61474123 and 61575207, the Science and Technology Innovation Foundation of Chinese Academy of Sciences under Grant No CXJJ-16S047, the National Defense Science and Technology Innovation Foundation of Chinese Academy of Sciences, the Program of International S&T Cooperation under Grant No 2016YFE0131500, and the Advance Research Project under Grant No 30102070101.
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https://cpl.iphy.ac.cn/10.1088/0256-307X/34/10/104203       OR      https://cpl.iphy.ac.cn/Y2017/V34/I10/104203
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Chao Wang
Xue-Feng Liu
Wen-Kai Yu
Xu-Ri Yao
Fu Zheng
Qian Dong
Ruo-Ming Lan
Zhi-Bin Sun
Guang-Jie Zhai
Qing Zhao
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