Research on compressed sensing spectral reconstruction   based on Fabry-Perot micro-array

WU Xiaonan; ZHANG Rui; WANG Zhibin; CHEN Zhuangzhuang

doi:10.5768/JAO202142.0301010

Journal of Applied Optics > 2021 > 42(3): 436-442. > DOI: 10.5768/JAO202142.0301010

WU Xiaonan, ZHANG Rui, WANG Zhibin, CHEN Zhuangzhuang. Research on compressed sensing spectral reconstruction based on Fabry-Perot micro-array[J]. Journal of Applied Optics, 2021, 42(3): 436-442. DOI: 10.5768/JAO202142.0301010

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Research on compressed sensing spectral reconstruction based on Fabry-Perot micro-array

School of Science, North University of China, Shanxi Photoelectric Information and Instrument Engineering Technology Research Center, Taiyuan 030051, China

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Received Date: November 02, 2020
Revised Date: March 02, 2021
Available Online: March 28, 2021

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Abstract

Abstract

Aiming at the problem that high throughput and high spectral resolution cannot be realized simultaneously in the existing space-borne scanning spectral imaging, a Fabry-Perot (FP) micro-array and compressed sensing spectral imaging method was proposed. By adding FP micro-arrays in front of the imaging detector, each unit of the FP micro-array modulator was corresponding to the different heights, and then the input optical signal was modulated to obtain the different spectral responses. Combined with the scanning and compressed sensing restoration algorithm, the hyper-spectral image data cube was finally obtained. The spectral range of the system was 400 nm~700 nm, and the number of spectral channels was as high as 700. By comparing the simulated laser incident spectrum with the compressed sensing reconstructed spectrum, the simulated mean square error (MSE) was 0.002. In addition, the two monochromatic light spectra with different colors were reconstructed by experiments. The experimental results were basically consistent with the spectrum measured by the standard spectrometer, which verified the feasibility of the spectrum reconstruction by this method. This method can be widely used in the measurements of high throughput, hyper-spectral space-borne or scanning spectral imaging.
- FP micro-array,
- spectral imaging,
- FP spectral modulation,
- compressed sensing,
- spectral data reconstruction

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References

[1]	解娜. 高光谱成像技术研究[D]. 吉林: 长春理工大学, 2019. XIE Na. Research on hyper-spectral imaging technology[D]. Jilin: Changchun University of Science and Technology, 2019.
[2]	REN W, FU C, WU D. Channeled compressive imaging spectropolarimeter[J]. Optics Express,2019,27(3):2197. doi: 10.1364/OE.27.002197
[3]	MENG X, LI J, LIU D, Fourier transform imaging spectropolarimeter using simultaneous polarization modulation[J]. Optics Letters, 2013, 38(5): 778-780.
[4]	李杰, 朱京平, 等. 静态傅里叶变换超光谱全偏振成像技术[J]. 物理学报,2013,62(04):185-190. LI Jie, ZHU Jingping. Static Fourier transform hyper-spectral full polarization imaging technology[J]. Journal of Physics,2013,62(04):185-190.
[5]	李杰, 朱京平, 等. 大孔径静态超光谱全偏振成像技术[J]. 红外与激光工程,2014,43(02):574-578. doi: 10.3969/j.issn.1007-2276.2014.02.044 LI Jie, ZHU Jingping, et al. Large aperture static hyperspectral full polarization imaging technology[J]. Infrared and Laser Engineering,2014,43(02):574-578. doi: 10.3969/j.issn.1007-2276.2014.02.044
[6]	张颖, 赵慧洁, 等. 基于LCVR调谐的全偏振多谱段成像系统[J]. 光谱学与光谱分析,2011,31(05):1375-1378. doi: 10.3964/j.issn.1000-0593(2011)05-1375-04 ZHANG Yin, ZHAO Huijie, et al. Full-polarization multi-spectral imaging system based on LCVR tuning[J]. Spectroscopy and Spectral Analysis,2011,31(05):1375-1378. doi: 10.3964/j.issn.1000-0593(2011)05-1375-04
[7]	石光明, 刘丹华, 高大化, 等. 压缩感知理论及其研究进展[J]. 电子学报,2009,37(05):1070-1081. doi: 10.3321/j.issn:0372-2112.2009.05.028 SHI Guangming, LIU Danhua, GAO Dahua, et al. Compressed sensing theory and its research progress[J]. Acta Electronica Sinica,2009,37(05):1070-1081. doi: 10.3321/j.issn:0372-2112.2009.05.028
[8]	任越美, 张艳宁, 李映. 压缩感知及其图像处理应用研究进展与展望[J]. 自动化学报,2014,40(08):1563-1575. REN Yuemei, ZHANG Yanning, LI Ying. Research progress and prospect of compressed sensing and its image processing application[J]. Journal of Automation,2014,40(08):1563-1575.
[9]	李浩. 用于压缩感知的确定性测量矩阵研究[D]. 北京: 北京交通大学, 2011. LI Hao. Research on deterministic measurement matrix for compressed sensing[D]. Beijing: Beijing Jiaotong University, 2011.
[10]	HE Z, WILLIAMSON Nn, SMITH C D, et al. Compressed single-shot hyperspectral imaging for combustion diagnostics[J]. Applied Optics,2020,59(17):5226-5233. doi: 10.1364/AO.390335
[11]	FINLEY M G, NISHIMURA J Y, BELL Tyler. Variable precision depth encoding for 3D range geometry compression[J]. Applied Optics,2020,59(17):5290-5299. doi: 10.1364/AO.389913
[12]	HUANG Jian, SHI Dongfeng, MENG Wenwen, et al. Spectral encoded computational ghost imaging[J]. Optics Communications,2020:474.
[13]	GEHM M E, JOHN R, BRADY D J. Single-shot compressive spectral imaging with a dual-disperser architecture[J]. Optics Express,2007,15(21):14013-14027. doi: 10.1364/OE.15.014013
[14]	WAGADARIKAR A, JOHN R, WILLETT R. Single disperser design for coded aperture snapshot spectral imaging[J]. Applied Optics,2008,47(10):B44-51. doi: 10.1364/AO.47.000B44
[15]	GOLUB M A, AVERBUCH A, NATHAN M, et al. Compressed sensing snapshot spectral imaging by a regular digital camera with an added optical diffuser[J]. Applied Optics, 2016, 55(3): 432.
[16]	刘铭鑫. 基于压缩感知的编码孔径光谱成像技术研究[D]. 北京: 中国科学院大学(中国科学院长春光学精密机械与物理研究所), 2019. LIU Mingxin. Research on the coding aperture spectral imaging technology based on compressed sensing[D]. Beijing: University of Chinese Academy of Sciences (Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences), 2019.
[17]	OIKNINE Y, AUGUST I, STERN A. Multi-aperture snapshot compressive hyperspectral camera[J]. Optics Letters, 2018, 43(20): 5042-5045.
[18]	QIAO Ya, JING Ning, ZHANG Rui, et al. Intensity modulation-based spectral polarization measurement method of coded aperture[J]. Optics Communications,2018,437:128-132.
[19]	YANG Xiulun, WU Huazheng, YIN Yongkai, et al. Multiple-image encryption base on compressed coded aperture imaging[J]. Optics and Lasers in Engineering,2020,127:105976.
[20]	OIKNINE Y, AUGUST I, BLUMBERG D G. Compressive sensing resonator spectroscopy[J]. Optics Letters[J]. 2017, 42(1): 25-28.
[21]	OIKNINE Y, AUGUST I, BLUMBERG D G, et al. NIR hyperspectral compressive imager based on a modified Fabry-Perot resonator[J]. Journal of Optics,2018,20(4).
[22]	刘博, 刘钧, 高明, 等. 光场多光谱相机像方远心镜头光学设计[J]. 应用光学,2020,41(3):603-610. doi: 10.5768/JAO202041.0305003 LIU Bo, LIU Jun, GAO Gaoming, et al. Optical design of image square telecentric lens for light field multispectral cameras[J]. Applied Optics,2020,41(3):603-610. doi: 10.5768/JAO202041.0305003
[23]	刘嘉楠. 微透镜阵列积分视场成像光谱仪的研究与设计[D]. 北京: 中国科学院大学(中国科学院长春光学精密机械与物理研究所), 2019. LIU Jianan. Research and design of integrated field imaging spectrometer with microlens array[D]. Beijing: University of Chinese Academy of Sciences (Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences), 2019.
[24]	庞阔. 基于微透镜阵列的光学成像系统设计与应用的研究[D]. 天津: 天津大学, 2017. PANG Kuo. A study on the design and application of optical imaging System based on microlens array[D]. Tianjin: Tianjin University, 2017.
[25]	CHAO Lianying, HAN Jiefei, YAN Lisong, et al. Fast compressed sensing analysis for imaging reconstruction with primal dual interior point algorithm[J]. Optics and Lasers in Engineering,2020,129:106082.
[26]	EMMANUEL J. The restricted isometry property and its implications for compressed sensing[J]. Comptes Rendus Mathématique,2008,346(9-10):589-592.
[27]	CANDES E J, ROMBERG J, TAO T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory,2006,52(2):489-509. doi: 10.1109/TIT.2005.862083
[28]	BIOUCAS-DIAS J M, FIGUEIREDO M A. A new twist: two-step iterative shrinkage /thresholding algorithms for image restoration[J]. IEEE Transactions on Image Process,2007,16(12):2992-3004. doi: 10.1109/TIP.2007.909319

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Research on compressed sensing spectral reconstruction based on Fabry-Perot micro-array