Compressed ultrahigh-speed single-pixel imaging by swept aggregate patterns

被引:50
作者
Kilcullen, Patrick [1 ]
Ozaki, Tsuneyuki [1 ]
Liang, Jinyang [1 ]
机构
[1] Univ Quebec, Inst Natl Rech Sci, Ctr Energie Mat Telecommun, 1650 Blvd Lionel Boulet, Varennes, PQ J3X 1P7, Canada
基金
加拿大创新基金会; 加拿大自然科学与工程研究理事会; 芬兰科学院;
关键词
DISK;
D O I
10.1038/s41467-022-35585-8
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Single-pixel imaging (SPI) has emerged as a powerful technique that uses coded wide-field illumination with sampling by a single-point detector. Most SPI systems are limited by the refresh rates of digital micromirror devices (DMDs) and time-consuming iterations in compressed-sensing (CS)-based reconstruction. Recent efforts in overcoming the speed limit in SPI, such as the use of fast-moving mechanical masks, suffer from low reconfigurability and/or reduced accuracy. To address these challenges, we develop SPI accelerated via swept aggregate patterns (SPI-ASAP) that combines a DMD with laser scanning hardware to achieve pattern projection rates of up to 14.1MHz and tunable frame sizes of up to 101x103 pixels. Meanwhile, leveraging the structural properties of S-cyclic matrices, a lightweight CS reconstruction algorithm, fully compatible with parallel computing, is developed for real-time video streaming at 100 frames per second (fps). SPI-ASAP allows reconfigurable imaging in both transmission and reflection modes, dynamic imaging under strong ambient light, and offline ultrahigh-speed imaging at speeds of up to 12,000 fps.
引用
收藏
页数:10
相关论文
共 58 条
[1]  
[Anonymous], 1979, Hadamard Transform Optics
[2]  
Babacan S. D., 2011, 2011 18th IEEE International Conference on Image Processing (ICIP 2011), P2705, DOI 10.1109/ICIP.2011.6116227
[3]   Experimental comparison of single-pixel imaging algorithms [J].
Bian, Liheng ;
Suo, Jinli ;
Dai, Qionghai ;
Chen, Feng .
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, 2018, 35 (01) :78-87
[4]  
Burden R. L., 1993, Numerical analysis, V5th
[5]  
Candès EJ, 2008, IEEE SIGNAL PROC MAG, V25, P21, DOI 10.1109/MSP.2007.914731
[6]   Deep learning for irregularly and regularly missing data reconstruction [J].
Chai, Xintao ;
Gu, Hanming ;
Li, Feng ;
Duan, Hongyou ;
Hu, Xiaobo ;
Lin, Kai .
SCIENTIFIC REPORTS, 2020, 10 (01)
[7]   Terahertz imaging with compressed sensing and phase retrieval [J].
Chan, Wai Lam ;
Moravec, Matthew L. ;
Baraniuk, Richard G. ;
Mittleman, Daniel M. .
OPTICS LETTERS, 2008, 33 (09) :974-976
[8]   A single-pixel terahertz imaging system based on compressed sensing [J].
Chan, Wai Lam ;
Charan, Kriti ;
Takhar, Dharmpal ;
Kelly, Kevin F. ;
Baraniuk, Richard G. ;
Mittleman, Daniel M. .
APPLIED PHYSICS LETTERS, 2008, 93 (12)
[9]   Imaging of hidden object using passive mode single pixel imaging with compressive sensing [J].
Chen, Qi ;
Chamoli, Sandeep Kumar ;
Yin, Peng ;
Wang, Xin ;
Xu, Xiping .
LASER PHYSICS LETTERS, 2018, 15 (12)
[10]  
Cohn J.H.E., 1963, Proc. Am. Math. Soc., V14, P581, DOI [10.1090/S0002-9939-1963-0151479-1, DOI 10.1090/S0002-9939-1963-0151479-1]