Review on infrared single-pixel imaging

• Autorzy: Szajewski Krzysztof , Szajewska Anna , Urbaś Sebastian , Więcek Bogusław

Identyfikatory

DOI

10.24425/opelre.2025.154306

• Języki publikacji: EN

Abstrakty

EN

The article discusses modern single-pixel imaging techniques. Different solutions of spatial light modulators (SLMs) used in infrared imaging are presented. The focus is on image reconstruction methods, in particular on the use of a modulator based on orthogonal codes, cyclic matrices, and neural networks for image reconstruction. The potential possibilities and limitations of these new imaging methods are described, emphasizing their usefulness in different ranges of the infrared spectrum. Moreover, the experimental implementation of a single-pixel infrared camera is presented. Possible applications and future development perspectives of this technology are indicated.

Słowa kluczowe

EN

image processing; single-pixel imaging; compressive sensing; thermal imaging; neural networks

• Wydawca: Stowarzyszenie Elektryków Polskich ; Polska Akademia Nauk ; Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
• Czasopismo: Opto-Electronics Review
• Rocznik: 2025
• Tom: Vol. 33, No. 2
• Strony: art. no. e154306
• Opis fizyczny: Bibliogr. 74 poz., rys., tab., fot.

Twórcy

autor

Szajewski Krzysztof

• Military University of Technology, ul. gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland

• https://orcid.org/0000-0002-8429-3976

autor

Szajewska Anna

• Fire University, ul. Juliusza Słowackiego 52/54, 01-629 Warsaw, Poland

• https://orcid.org/0000-0001-8370-6807

autor

Urbaś Sebastian

• Institute of Electronics, Lodz University of Technology, al. Politechniki 8, 90–590 Lodz, Poland

• https://orcid.org/0000-0002-7699-5425

autor

Więcek Bogusław

• Institute of Electronics, Lodz University of Technology, al. Politechniki 8, 90–590 Lodz, Poland

• https://orcid.org/0000-0002-5003-1687

Bibliografia

• [1] Osorio Quero, C. A., Durini, D., Rangel-Magdaleno, J. & Martinez-Carranza, J. Single-pixel imaging: An overview of different methods to be used for 3D space reconstruction in harsh environments. Rev. Sci. Instrum. 92, 111501 (2021). https://doi.org/10.1063/5.0050358.
• [2] Beckman, J. E. Infrared Astronomy. in Multimessenger Astronomy 81-115 (Springer International Publishing, 2021). https://doi.org/10.1007/978-3-030-68372-6_3.
• [3] Rogalski, A. History of infrared detectors. Opto-Electron. Rev. 20, 279-308 (2012). https://doi.org/10.2478/s11772-012-0037-7.
• [4] Stupp, E. H., Crowell, M. H. & Singer, B. Pyroelectric Vidicons. in 1972 International Electron Devices Meeting 156 (Springer, 1972) https://doi.org/10.1109/IEDM.1972.249374.
• [5] Talmi, Y. Pyroelectric vidicon: A new multichannel spectrometric infrared (1.0-30-μm) detector. Appl. Opt. 17, 2489-2501 (1978). https://doi.org/10.1364/AO.17.002489.
• [6] Fièque, B., Tissot, J. L., Trouilleau, C., Crastes, A. & Legras, O. Uncooled microbolometer detector. Recent developments at ULIS. Infrared Phys. Technol. 49, 187-191 (2007). https://doi.org/10.1016/j.infrared.2006.06.030.
• [7] Xia, H., Nguyen, H., Roy, A., Ohlckers, P. & Aasmundtveit, K. E. Robustness of large-size vacuum sealed packages for micro-bolometer array. IEEE Trans. Compon. Packag. Manuf. Technol. 14, 1731-1736 (2024). https://doi.org/10.1109/TCPMT.2024.3462818.
• [8] Zoschke, K. et al. Wafer Level Capping Technology for Vacuum Packaging of Microbolometers. in 2023 IEEE 73rd Electronic Components and Technology Conference (ECTC) 1571-1578 (IEEE, 2023). https://doi.org/10.1109/ECTC51909.2023.00267.
• [9] Gibson, G. M., Johnson, S. D. & Padgett, M. J. Single-pixel imaging 12 years on: A review. Opt. Express 28, 28190-28208 (2020). https://doi.org/10.1364/OE.403195.
• [10] Brodie, C. H. & Collier, C. M. Single-point detection architecture via liquid crystal modulation for hyperspectral imaging systems. IEEE Access. 8, 185012-185020 (2020). https://doi.org/10.1109/ACCESS.2020.3029550.
• [11] Huang, G., Jiang, H., Matthews, K. & Wilford, P. Lensless Imaging by Compressive Sensing. in 2013 IEEE International Conference on Image Processing 2101-2105 (IEEE, 2013). https://doi.org/10.1109/ICIP.2013.6738433.
• [12] Toyoda, H., Inoue ,T. & Hara, T. Application of Liquid Crystal on Silicon Spatial Light Modulator (LCOS-SLM) for Manipulation and Sensing. in 2015 14th Workshop on Information Optics (WIO) 1-3 (IEEE, 2015). https://doi.org/10.1109/WIO.2015.7206900.
• [13] Duarte, M. F. et al. Single-pixel imaging via compressive sampling. IEEE Signal Process. Mag. 25, 83-91 (2008). https://doi.org/10.1109/MSP.2007.914730.
• [14] Radwell, N. et al. Single-pixel infrared and visible microscope. Optica 1, 285-289 (2014). https://doi.org/10.1364/optica.1.000285.
• [15] Yu, W. et al. Complementary compressive imaging for the telescopic system. Sci. Rep. 4, 5834 (2014). https://doi.org/10.1038/srep05834.
• [16] Toninelli, E., Stellinga, D., Sephton, B. Forbes, A. & Padgett, M. J. Single-pixel imaging using caustic patterns. Sci. Rep. 10, 2281 (2020). https://doi.org/10.1038/s41598-020-59224-8.
• [17] Griffiths, A. D. et al. Hyperspectral Imaging under Low Illumination with a Single Photon Camera. in 2018 IEEE British and Irish Conference on Optics and Photonics (BICOP) 1-4 (IEEE, 2018). https://doi.org/10.1109/BICOP.2018.8658323.
• [18] Jain, R., Hillger P., Grzyb, J. & Pfeiffer, U. R. Silicon-Integrated Single Pixel Terahertz Camera. in 45th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz) 1-2 (IEEE, 2020). https://doi.org/10.1109/IRMMW-THz46771.2020.9370707.
• [19] Hahamovich, E., Monin, S., Hazan, Y. & Rosenthal, A. Single pixel imaging at megahertz switching rates via cyclic Hadamard masks. Nat. Commun. 12, 4516 (2021). https://doi.org/10.1038/s41467-021-24850-x.
• [20] Chan, W. L. et al. A Single-Pixel Terahertz Camera. in Conference on Lasers and Electro-Optics and 2008 Conference on Quantum Electronics and Laser Science 1-2 (IEEE, 2008). https://doi.org/10.1109/CLEO.2008.4551487.
• [21] Zhang, Z. et al. A decade review of video compressive sensing: A roadmap to practical applications. Engineering 46, 172-185 (2025). https://doi.org/10.1016/j.eng.2024.08.013.
• [22] Han, S., Sato, I., Okabe, T. & Sato, Y. Fast spectral reflectance recovery using DLP projector. Int. J. Comput. Vis. 110, 172-184 (2014). https://doi.org/10.1007/s11263-013-0687-z.
• [23] Jin, S. et al. Hyperspectral imaging using the single-pixel Fourier transform technique. Sci Rep. 7, 45209 (2017). https://doi.org/10.1038/srep45209.
• [24] Edgar, M. P. et al. Simultaneous real-time visible and infrared video with single-pixel detectors. Sci. Rep. 5, 10669 (2015). https://doi.org/10.1038/srep10669.
• [25] Harwit, M. & Sloane, N. Hadamard Transform Optics. (Academic Press New York, 1979).
• [26] Candès, E. J. & Tao, T. Near optimal signal recovery from random projections: Universal encoding strategies? IEEE Trans. Inf. Theory 52, 5406-5425 (2006). https://doi.org/10.1109/TIT.2006.885507.
• [27] Candes, E. J., Romberg, J. & Tao, T. Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59, 1207-1223 (2006). https://doi.org/10.1002/cpa.20124.
• [28] Candes, E. J. & Wakin, M. B. An introduction to compressive sampling. IEEE Signal Process. Mag. 25, 21 (2008). https://doi.org/10.1109/MSP.2007.914731.
• [29] Balpande, A., Arora, J., Dhote K. & Khandelwal, R. Spatial Light Modulation Pattern Generation by Golomb Code for Single Pixel Imaging. in 2022 IEEE Region 10 Symposium (TENSYMP) 1-5 (IEEE, 2022). https://doi.org/10.1109/TENSYMP54529.2022.9864522.
• [30] Pastuszczak, A., Stojek, R., Wróbel, P. & Kotynski, R. Differential real-time single-pixel imaging with Fourier domain regularization -applications to VIS-IR imaging and polarization imaging. Opt. Express 29, 2685-26700 (2021). https://doi.org/10.1364/OE.433199.
• [31] Szajewski, K., Szajewska, A., Urbaś, S., Olbrycht, R. & Więcek, B. Imaging in NIR Spectral Range with A Single Detector Using - Pattern-Based SLM. in 17th Quantitative Infrared Thermography Conference (QIRT) (2024). https://qirt2024.org/assets/sazetak/QIRT-2024-046.pdf.
• [32] Bahmani, S. & Romberg J. compressive deconvolution in random mask imaging. IEEE Trans. Comput. Imaging 1, 236-246 (2015). https://doi.org/10.1109/TCI.2015.2485941.
• [33] Don, M. L., Fu, C. & Arce, G. R. Compressive imaging via a rotating coded aperture. Appl. Opt. 56, B142-B153 (2017). https://doi.org/10.1364/AO.56.00B142.
• [34] Candès, E. J., Romberg, J. & Tao, T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52, 489-509 (2006). https://doi.org/10.1109/TIT.2005.862083.
• [35] Hema, M., Gurunadha, R., Suman, J. V. & Mallam, M. Effective Image Reconstruction Using Various Compressed Sensing Techniques. in 2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE) 1-6 (IEEE, 2024). https://doi.org/10.1109/AMATHE61652.2024.10582191.
• [36] Baraniuk, R. G. Compressive Sensing [Lecture Notes]. IEEE Signal Process. Mag. 24, 118-121 (2007). https://doi.org/10.1109/MSP.2007.4286571.
• [37] Huo, L., Chen, W., Ge, H. & Ng, M. K. Stable image reconstruction using transformed total variation minimization. SIAM J. Imaging Sci. 15, 1104-1139 (2022). https://doi.org/10.1137/21M1438566.
• [38] Gao, L., Zhai, A. & Wang, D. Comparison of common algorithms for single-pixel imaging via compressed sensing. Sensors 23, 4678 (2023). https://doi.org/10.3390/s23104678.
• [39] Bian, L., Suo J., Dai, Q. & Chen, F. Experimental comparison of single-pixel imaging algorithms. J. Opt. Soc. Am. A 35, 78-87 (2018). https://doi.org/10.1364/JOSAA.35.000078.
• [40] Satake, S. & Gu, Y. On Compressed Sensing Matrices Breaking The Square-Root Bottleneck. in 2020 IEEE Information Theory Workshop (ITW) 1-5 (IEEE, 2021). https://doi.org/10.1109/ITW46852.2021.9457623.
• [41] Lampe, B., Chang, C.-I., Bekit, A. & Porta, C. D. Restricted entropy and spectrum properties for the compressively sensed domain in hyperspectral imaging. IEEE Trans. Geosci. Remote Sens. 58, 5642-5652 (2020). https://doi.org/10.1109/TGRS.2020.2968077.
• [42] Gan, L., Li, K. & Ling, C. Golay meets Hadamard: Golay-paired Hadamard matrices for fast compressed sensing. in IEEE Information Theory Workshop 637-641 (IEEE, 2012). https://doi.org/10.1109/ITW.2012.6404755.
• [43] Lim, J. Y. et al. A comparison between fourier and Hadamard single-pixel imaging in deep learning-enhanced image reconstru-ction. IEEE Sens. Lett. 7, 1-4 (2023). https://doi.org/10.1109/LSENS.2023.3303046.
• [44] Liu, Z., Li, D., Yan, P., Gong, M. & Xiao, Q. Single-Pixel High-Speed Imaging Through A Multimode Fiber. in 2022 Conference on Lasers and Electro-Optics (CLEO) JW3B.56 (2022). https://doi.org/10.1364/CLEO_AT.2022.JW3B.56.
• [45] Li, X., Li, J., Li, Y. & Xiao, Q. High-throughput terahertz imaging: progress and challenges. Light: Sci. Appl. 12, 233 (2023). https://doi.org/10.1038/s41377-023-01278-0.
• [46] Zanotto, L., Piccoli, R., Dong, J., Morandotti, R. & Razzari, L. Single-pixel terahertz imaging: A review. Opto-Electron. Adv. 3, 200012 (2020). https://doi.org/10.29026/oea.2020.200012.
• [47] Singh, P. & Sonkusale, S. high speed terahertz modulator on the chip based on tunable terahertz slot waveguide. Sci. Rep. 7, 40933 (2017). https://doi.org/10.1038/srep40933.
• [48] Ma, W., Lin, Z., Sun, Y. & Liu, X. Multi-Color Terahertz Spatial Light Modulator for Single-pixel Imaging. in 2024 49th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz) 1-2 (IEEE, 2024). https://doi.org/10.1109/IRMMW-THz60956.2024.10697881.
• [49] Shrekenhamer, D., Watts, C. M. & Padilla, W. J. Terahertz single pixel imaging with an optically controlled dynamic spatial light modulator. Opt. Express 21, 12507-12518 (2013). https://doi.org/10.1364/OE.21.012507.
• [50] Einstein, A., Podolsky, B. & Rosen, N. Can quantum-mechanical description of physical reality be considered complete? Phys. Rev. 47, 777-780 (1935). https://doi.org/10.1103/PhysRev.47.777.
• [51] Pittman, T. B., Shih, Y. H., Strekalov, D. V. & Sergienko, A. V. Optical imaging by means of two photon quantum entanglement. Phys. Rev. A 52, 3429-3432 (1995). https://doi.org/10.1103/PhysRevA.52.R3429.
• [52] Shapiro, J. H. & Boyd, R. W. The physics of ghost imaging. Quantum Inf. Process. 11, 949-993 (2012). https://doi.org/10.1007/s11128-011-0356-5.
• [53] Bennink, R. S., Bentley, S. J. & Boyd, R. W. ‘Two-photon’ coincidence imaging with a classical source. Phys. Rev. Lett. 89, 113601 (2002). https://doi.org/10.1103/PhysRevLett.89.113601.
• [54] Bennink, R. S., Bentley, S. J., Boyd, R. W. & Howell, J. C. Quantum and classical coincidence imaging. Phys. Rev. Lett. 92, 33601 (2004). https://doi.org/10.1103/PhysRevLett.92.033601.
• [55] Moodley, C. & Forbes, A. Super-resolved quantum ghost imaging. Sci. Rep. 12, 10346 (2022). https://doi.org/10.1038/s41598-022-14648-2.
• [56] Gatti, A., Brambilla, E., Bache, M. & Lugiato, L. A. Ghost imaging with thermal light: comparing entanglement and classical correlation. Phys. Rev. Lett. 93, 93602 (2004). https://doi.org/10.1103/PhysRevLett.93.093602.
• [57] Padgett, M. J. & Boyd, R. W. An introduction to ghost imaging: Quantum and classical. Philos. Trans. R. Soc. A 375, 20160233 (2017). https://doi.org/10.1098/rsta.2016.0233.
• [58] Liu, Y., Gutierrez-Barragan, F., Ingle, A. & Velten, A. Single-Photon Camera Guided Extreme Dynamic Range Imaging. in 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 41-51 (IEEE, 2022). https://doi.org/10.1109/WACV51458.2022.00012.
• [59] Chapman, G. H., Coelho Silva Menes, K. J., Wu, L. Y., Koren I. & Koren, Z. Image Degradation in Time Due to Interacting Hot Pixels. in 2023 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT) 1-6 (IEEE, 2023). https://doi.org/10.1109/DFT59622.2023.10313555.
• [60] Wu, J., Hu, L. & Wang, J. Single-pixel imaging for partially occluded objects. IEEE Photonics J. 15, 1-7 (2023). https://doi.org/10.1109/JPHOT.2023.3281820.
• [61] Vaz, P. G., Guerra, B. & Cardoso, J. Single-pixel imaging: Concepts and application to imaging through scattering media. in 2023 23rd International Conference on Transparent Optical Networks (ICTON) 1-9 (IEEE, 2023). https://doi.org/10.1109/ICTON59386.2023.10207213.
• [62] Deng, Z. et al. Seeing through fire with one pixel. Opt. Lasers Eng. 183, 108540 (2024). https://doi.org/10.1016/j.optlaseng.2024.108540.
• [63] Nan, Y., Yi, Z. & Bingxia, C. Review of Compressed Sensing for Biomedical Imaging. in 2015 7th International Conference on Information Technology in Medicine and Education (ITME) 225-228 (IEEE, 2015). https://doi.org/10.1109/ITME.2015.119.
• [64] Kim, K. et al. Adaptive compressed sensing for the fast terahertz reflection tomography. IEEE Trans. Terahertz Sci. Technol. 3, 395-401 (2013). https://doi.org/10.1109/TTHZ.2013.2267417.
• [65] Folks, W. R. et al. Characterization of digital-micromirror device-based infrared scene projector. Opt. Eng. 44, 086402 (2005). https://doi.org/10.1117/1.2013249.
• [66] Gattinger, P., Zorin, I., Ebner, A., Rankl, Ch. & Brandstetter, M. Mid-infrared DMD-based spectral-coding spectroscopy with a supercontinuum laser source. Opt. Express. 30, 6440-6449 (2022). https://doi.org/10.1364/OE.452221.
• [67] Mahalanobis, A., Shilling, R., Murphy, R. & Muise, R. Recent results of medium wave infrared compressive sensing. Appl. Opt. 53, 8060-8070 (2014). https://doi.org/10.1364/ao.53.008060.
• [68] Gibson, G. M. et al. Real-timeimaging of methane gas leaks using a single-pixel camera. Opt. Express 25, 2998-3005 (2017). https://doi.org/10.1364/OE.25.002998.
• [69] Denk, O., Musiienko, A. & Žídek, K. Differential single-pixel camera enabling low-cost microscopy in near-infrared spectral region. Opt. Express 27, 4562 (2019). https://doi.org/10.1364/OE.27.004562.
• [70] Stantchev, R. I., Yu, X., Blu, T. & Pickwell-MacPherson, E. Real-time terahertz imaging with a single-pixel detector. Nat. Commun. 11, 2535 (2020). https://doi.org/10.1038/s41467-020-16370-x.
• [71] Jin, S. et al. Hyperspectral imaging using the single-pixel Fourier transform technique. Sci. Rep. 7, 45209 (2017). https://doi.org/10.1038/srep45209.
• [72] Więcek, P. & Sankowski, D. A New Deep-Learning Neural Network for Super-Resolution Up-Scaling of Thermal Images. in 15th Quantitative Infrared Thermography Conference 1-7 QIRT Conference Proc. 1-7 (QIRT, 2020). http://www.qirt.org/archives/qirt2020/papers/134.pdf.
• [73] Urbaś, S. & Więcek, B. Application of a deep-learning neural network for image reconstruction from a single-pixel infrared camera. Opto-Electron. Rev. 32, e148877 (2024). https://doi.org/10.24425/opelre.2024.148877.
• [74] https://www.tensorflow.org/ (2024).

Uwagi

1. This work was financed/co-financed by Military University of Technology under research project UGB 4720600000-531-0024-W500-22-W523000. This article has been completed while the third author was the Doctoral Candidate in the Interdisciplinary Doctoral School at the Lodz University of Technology, Poland.

2. Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).