Cascaded compressed-sensing single-pixel camera for high-dimensional optical imaging

Department of Bioengineering, University of California, Los Angeles, CA, 90095, USA

Funds: This work was supported partially by National Institutes of Health (R35GM128761).

摘要

- /
- /
- /
- /
- /
- /
- /
- /
- /
- /
- /
Abstract: Single-pixel detectors are popular devices in optical sciences because of their fast temporal response, high sensitivity, and low cost. However, when being used for imaging, they face a fundamental challenge in acquiring high-dimensional information of an optical field because they are essentially zero-dimensional sensors and measure only the light intensity. To address this problem, we developed a cascaded compressed-sensing single-pixel camera, which decomposes the measurement into multiple stages, sequentially reducing the dimensionality of the data from a high-dimensional space to zero dimension. This measurement scheme allows us to exploit the compressibility of a natural scene in multiple domains, leading to highly efficient data acquisition. We demonstrated our method in several demanding applications, including enabling tunable single-pixel full-waveform hyperspectral light detection and ranging (LIDAR) for the first time.

HTML全文

参考文献(47)

[1]	Edgar MP, Gibson GM, Padgett MJ. Principles and prospects for single-pixel imaging. Nat Photonics. 2019;13:13–20.
[2]	Gibson GM, Johnson SD, Padgett MJ. Single-pixel imaging 12 years on: a review. Opt Express. 2020;28:28190–208.
[3]	Wu G, et al. Light field image processing: an overview. IEEE J Sel Top Signal Process. 2017;11:926–54.
[4]	Savage N. Digital spatial light modulators. Nat Photonics. 2009;3:170–2.
[5]	Shapiro JH. Computational ghost imaging. Phys Rev A. 2008;78:061802.
[6]	Duarte MF, et al. Single-pixel imaging via compressive sampling. IEEE Signal Process Mag. 2008;25:83–91.
[7]	Chan WL, et al. A single-pixel terahertz imaging system based on compressed sensing. Appl Phys Lett. 2008;93:121105.
[8]	Studer V, et al. Compressive fluorescence microscopy for biological and hyperspectral imaging. Proc Natl Acad Sci. 2012;109:E1679–87.
[9]	Zhang Z, Ma X, Zhong J. Single-pixel imaging by means of Fourier spectrum acquisition. Nat Commun. 2015;6:1–6.
[10]	Edgar M, et al. Simultaneous real-time visible and infrared video with single-pixel detectors. Sci Rep. 2015;5:1–8.
[11]	Sun M-J, et al. Single-pixel three-dimensional imaging with time-based depth resolution. Nat Commun. 2016;7:1–6.
[12]	Yu H, et al. Fourier-transform ghost imaging with hard X rays. Phys Rev Lett. 2016;117:113901.
[13]	Stockton P, et al. Tomographic single pixel spatial frequency projection imaging. Opt Commun. 2022;520;021907.
[14]	Candes EJ, Tao T. Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Trans Inf Theory. 2006;52:5406–25.
[15]	Torabzadeh M, Park I-Y, Bartels RA, Durkin AJ, Tromberg BJ. Compressed single pixel imaging in the spatial frequency domain. J Biomed Opt. 2017;22:030501.
[16]	Horadam KJ. Hadamard matrices and their applications. Hadamard matrices and their applications. New Jersey: Princeton University Press; 2012.
[17]	Zhang Z, Wang X, Zheng G, Zhong J. Hadamard single-pixel imaging versus Fourier single-pixel imaging. Opt Express. 2017;25:19619–39.
[18]	Dudley D, Duncan WM, Slaughter J. Emerging digital micromirror device (DMD) applications. MOEMS Display Imaging Syst. 2003;4985:14–25 (International Society for Optics and Photonics).
[19]	Sun MJ, Meng LT, Edgar MP, Padgett MJ, Radwell N. A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging. Sci Rep. 2017;7:1–7.
[20]	López-García L, et al. Efficient ordering of the Hadamard basis for single pixel imaging. Opt Express. 2022;30:13714–32.
[21]	Duarte MF, Eldar YC. Structured compressed sensing: from theory to applications. IEEE Trans Signal Process. 2011;59:4053–85.
[22]	Dong W, Shi G, Li X, Ma Y, Huang F. Compressive sensing via nonlocal low-rank regularization. IEEE Trans Image Process. 2014;23:3618–32.
[23]	Yu X, Stantchev RI, Yang F, Pickwell-MacPherson E. Super sub-nyquist single-pixel imaging by total variation ascending ordering of the hadamard basis. Sci Rep. 2020;10:1–11.
[24]	Gao L, Smith RT. Optical hyperspectral imaging in microscopy and spectroscopy–a review of data acquisition. J Biophotonics. 2015;8:441–56.
[25]	Park J, Feng X, Liang R, Gao L. Snapshot multidimensional photography through active optical mapping. Nat Commun. 2020;11:1–13.
[26]	Liang J, Wang P, Zhu L, Wang LV. Single-shot stereo-polarimetric compressed ultrafast photography for light-speed observation of high-dimensional optical transients with picosecond resolution. Nat Commun. 2020;11:5252.
[27]	Zhao Z, et al. Redundant compressed single-pixel hyperspectral imaging system. Opt Commun. 2023;546:129797.
[28]	Jin S, et al. Hyperspectral imaging using the single-pixel fourier transform technique. Sci Rep. 2017;7:1–7.
[29]	Bian L, et al. Multispectral imaging using a single bucket detector. Sci Rep. 2016;6:1–7.
[30]	Sun B, et al. 3D computational imaging with single-pixel detectors. Science. 2013;340:844–7.
[31]	Helgason S, Helgason S. The radon transform, vol. 2. New York: Springer; 1980.
[32]	Feng X, Gao L. Ultrafast light field tomography for snapshot transient and non-line-of-sight imaging. Nat Commun. 2021;12:2179.
[33]	Ma Y, et al. Light-field tomographic fluorescence lifetime imaging microscopy. Proc Natl Acad Sci. 2024;121:e2402556121.
[34]	Li X, Luo S. A compressed sensing-based iterative algorithm for CT reconstruction and its possible application to phase contrast imaging. Biomed Eng OnLine. 2011;10:73.
[35]	Kudo H, Suzuki T, Rashed EA. Image reconstruction for sparse-view CT and interior CT—introduction to compressed sensing and differentiated backprojection. Quant Imaging Med Surg. 2013;3:147–61.
[36]	Candes E, Romberg J. Sparsity and incoherence in compressive sampling. Inverse Probl. 2007;23:969.
[37]	Xu J, Pi Y, Cao Z. Optimized projection matrix for compressive sensing. EURASIP J Adv Signal Process. 2010;2010:1–8.
[38]	Abo-Zahhad MM, Hussein AI, Mohamed AM. Compressive sensing algorithms for signal processing applications: a survey. Int J Commun Netw Syst Sci. 2015;8:197.
[39]	Arjoune Y, Kaabouch N, Ghazi E, Tamtaoui A. A performance comparison of measurement matrices in compressive sensing. Int J Commun Syst. 2018;31:e3576.
[40]	Nouasria H, Et-tolba M, Bedoui A. New sensing matrices based On orthogonal hadamard matrices for compressive sensing. in 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC). IEEE; 2019. p. 186–91.
[41]	Beck A, Teboulle M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci. 2009;2:183–202.
[42]	Higham CF, Murray-Smith R, Padgett MJ, Edgar MP. Deep learning for real-time single-pixel video. Sci Rep. 2018;8:1–9.
[43]	Li Z, et al. Efficient single-pixel multispectral imaging via non-mechanical spatio-spectral modulation. Sci Rep. 2017;7:41435.
[44]	Tzang O, et al. Wavefront shaping in complex media with a 350 kHz modulator via a 1D-to-2D transform. Nat Photonics. 2019;13:788–93.
[45]	Shaltout AM, Shalaev VM, Brongersma ML. Spatiotemporal light control with active metasurfaces. Science. 2019;364:eaat300.
[46]	Oliva E. Wedged double Wollaston, a device for single shot polarimetric measurements. Astron Astrophys Suppl Ser. 1997;123:589–92.
[47]	Luo Y, et al. Laser-induced fluorescence imaging of subsurface tissue structures with a volume holographic spatial-spectral imaging system. Opt Lett. 2008;33:2098–100.