• Advanced Photonics Nexus
  • Vol. 4, Issue 3, 036005 (2025)
Yifei Zhang1, Yingxin Li1, Zonghao Liu1, Fei Wang3..., Guohai Situ3, Mu Ku Chen4, Haoqiang Wang5 and Zihan Geng1,2,*|Show fewer author(s)
Author Affiliations
  • 1Tsinghua University, Tsinghua Shenzhen International Graduate School, Shenzhen, China
  • 2Pengcheng Laboratory, Shenzhen, China
  • 3Chinese Academy of Sciences, Shanghai Institute of Optics and Fine Mechanics, Shanghai, China
  • 4City University of Hong Kong, Department of Electrical Engineering, Hong Kong, China
  • 5Shenzhen University, College of Physics and Optoelectronic Engineering, Shenzhen, China
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    DOI: 10.1117/1.APN.4.3.036005 Cite this Article Set citation alerts
    Yifei Zhang, Yingxin Li, Zonghao Liu, Fei Wang, Guohai Situ, Mu Ku Chen, Haoqiang Wang, Zihan Geng, "Physics and data-driven alternative optimization enabled ultra-low-sampling single-pixel imaging," Adv. Photon. Nexus 4, 036005 (2025) Copy Citation Text show less
    References

    [1] M. P. Edgar, G. M. Gibson, M. J. Padgett. Principles and prospects for single-pixel imaging. Nat. Photonics, 13, 13-20(2019). https://doi.org/10.1038/s41566-018-0300-7

    [2] Y. Wang et al. Mid-infrared single-pixel imaging at the single-photon level. Nat. Commun., 14, 1073(2023). https://doi.org/10.1038/s41467-023-36815-3

    [3] B. I. Erkmen, J. H. Shapiro. Ghost imaging: from quantum to classical to computational. Adv. Opt. Photonics, 2, 405-450(2010). https://doi.org/10.1364/AOP.2.000405

    [4] T. B. Pittman et al. Optical imaging by means of two-photon quantum entanglement. Phys. Rev. A, 52, R3429(1995). https://doi.org/10.1103/PhysRevA.52.R3429

    [5] M. F. Duarte et al. Single-pixel imaging via compressive sampling. IEEE Signal Process Mag., 25, 83-91(2008). https://doi.org/10.1109/MSP.2007.914730

    [6] G. M. Gibson, S. D. Johnson, M. J. Padgett. Single-pixel imaging 12 years on: a review. Opt. Express, 28, 28190-28208(2020). https://doi.org/10.1364/OE.403195

    [7] R. I. Stantchev et al. Real-time terahertz imaging with a single-pixel detector. Nat. Commun., 11, 2535(2020). https://doi.org/10.1038/s41467-020-16370-x

    [8] Y. Guo, B. Li, X. Yin. Dual-compressed photoacoustic single-pixel imaging. Natl. Sci. Rev., 10, nwac058(2023). https://doi.org/10.1093/nsr/nwac058

    [9] H. Liu, L. Bian, J. Zhang. Image-free single-pixel segmentation. Opt. Laser Technol., 157, 108600(2023). https://doi.org/10.1016/j.optlastec.2022.108600

    [10] A. Tsoy et al. Image-free single-pixel keypoint detection for privacy preserving human pose estimation. Opt. Lett., 49, 546-549(2024). https://doi.org/10.1364/OL.514213

    [11] H. Zhang et al. Ultra-efficient single-pixel tracking and imaging of moving objects based on geometric moment. Proc. SPIE, 12617, 126177N(2023). https://doi.org/10.1117/12.2666863

    [12] A.-X. Zhang et al. Tabletop X-ray ghost imaging with ultra-low radiation. Optica, 5, 374-377(2018). https://doi.org/10.1364/OPTICA.5.000374

    [13] L. Zanotto et al. Single-pixel terahertz imaging: a review. Opto-Electron. Adv., 3, 200012(2020). https://doi.org/10.29026/oea.2020.200012

    [14] J. Wang et al. Single-pixel p-graded-n junction spectrometers. Nat. Commun., 15, 1773(2024). https://doi.org/10.1038/s41467-024-46066-5

    [15] Z. Qiu et al. Comprehensive comparison of single-pixel imaging methods. Opt. Lasers Eng., 134, 106301(2020). https://doi.org/10.1016/j.optlaseng.2020.106301

    [16] E. J. Candes, J. K. Romberg, T. Tao. Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math., 59, 1207-1223(2006). https://doi.org/10.1002/cpa.20124

    [17] O. Katz, Y. Bromberg, Y. Silberberg. Compressive ghost imaging. Appl. Phys. Lett., 95, 131110(2009). https://doi.org/10.1063/1.3238296

    [18] L. Bian et al. Experimental comparison of single-pixel imaging algorithms. JOSA A, 35, 78-87(2018). https://doi.org/10.1364/JOSAA.35.000078

    [19] Z. Zhang, X. Ma, J. Zhong. Single-pixel imaging by means of Fourier spectrum acquisition. Nat. Commun., 6, 6225(2015). https://doi.org/10.1038/ncomms7225

    [20] F. Ferri et al. Differential ghost imaging. Phys. Rev. Lett., 104, 253603(2010). https://doi.org/10.1103/PhysRevLett.104.253603

    [21] Y. Zhang et al. Single-pixel imaging robust to arbitrary translational motion. Opt. Lett., 49, 6892-6895(2024). https://doi.org/10.1364/OL.531122

    [22] X. Zhu et al. Adaptive real-time single-pixel imaging. Opt. Lett., 49, 1065-1068(2024). https://doi.org/10.1364/OL.514934

    [23] H. K. Aggarwal, M. P. Mani, M. Jacob. MoDL: model-based deep learning architecture for inverse problems. IEEE Trans. Med. Imaging, 38, 394-405(2018). https://doi.org/10.1109/TMI.2018.2865356

    [24] C. F. Higham et al. Deep learning for real-time single-pixel video. Sci. Rep., 8, 2369(2018). https://doi.org/10.1038/s41598-018-20521-y

    [25] M. Lyu et al. Deep-learning-based ghost imaging. Sci. Rep., 7, 17865(2017). https://doi.org/10.1038/s41598-017-18171-7

    [26] Y. Tian, Y. Fu, J. Zhang. Local-enhanced transformer for single-pixel imaging. Opt. Lett., 48, 2635-2638(2023). https://doi.org/10.1364/OL.483877

    [27] S. Mao et al. High-quality and high-diversity conditionally generative ghost imaging based on denoising diffusion probabilistic model. Opt. Express, 31, 25104-25116(2023). https://doi.org/10.1364/OE.496706

    [28] Z. Huang et al. Imaging quality enhancement in photon-counting single-pixel imaging via an ADMM-based deep unfolding network in small animal fluorescence imaging. Opt. Express, 32, 27382-27398(2024). https://doi.org/10.1364/OE.529829

    [29] X. Li et al. Part-based image-loop network for single-pixel imaging. Opt. Laser Technol., 168, 109917(2024). https://doi.org/10.1016/j.optlastec.2023.109917

    [30] Z. Liu et al. Adaptive super-resolution networks for single-pixel imaging at ultra-low sampling rates. IEEE Access, 12, 78496-78504(2024). https://doi.org/10.1109/ACCESS.2024.3402693

    [31] J. Lim et al. Enhancing single-pixel imaging reconstruction using hybrid transformer network with adaptive feature refinement. Opt. Express, 32, 32370-32386(2024). https://doi.org/10.1364/OE.523276

    [32] F. Wang et al. Far-field super-resolution ghost imaging with a deep neural network constraint. Light Sci. Appl., 11, 1(2022). https://doi.org/10.1038/s41377-021-00680-w

    [33] S. Liu et al. Computational ghost imaging based on an untrained neural network. Opt. Lasers Eng., 147, 106744(2021). https://doi.org/10.1016/j.optlaseng.2021.106744

    [34] X. Chang et al. Self-supervised learning for single-pixel imaging via dual-domain constraints. Opt. Lett., 48, 1566-1569(2023). https://doi.org/10.1364/OL.483886

    [35] F. Wang et al. Single-pixel imaging using physics enhanced deep learning. Photonics Res., 10, 104-110(2022). https://doi.org/10.1364/PRJ.440123

    [36] X. Zhang et al. VGenNet: variable generative prior enhanced single pixel imaging. ACS Photonics, 10, 2363-2373(2023). https://doi.org/10.1021/acsphotonics.2c01537

    [37] D.-Y. Wang et al. Single-pixel infrared hyperspectral imaging via physics-guided generative adversarial networks. Photonics, 11, 174(2024). https://doi.org/10.3390/photonics11020174

    [38] A. Sholokhov et al. Single-pixel imaging of dynamic flows using neural ode regularization, 2530-2534(2024). https://doi.org/10.1109/ICASSP48485.2024.10447584

    [39] J. Li et al. URNet: high-quality single-pixel imaging with untrained reconstruction network. Opt. Lasers Eng., 166, 107580(2023). https://doi.org/10.1016/j.optlaseng.2023.107580

    [40] W.-K. Yu, S.-F. Wang, K.-Q. Shang. Optical encryption using attention-inserted physics-driven single-pixel imaging. Sensors, 24, 1012(2024). https://doi.org/10.3390/s24031012

    [41] L. Yang et al. Diffusion models: a comprehensive survey of methods and applications. ACM Comput. Surv., 56, 1-39(2023). https://doi.org/10.1145/3626235

    [42] X. Song et al. High-resolution iterative reconstruction at extremely low sampling rate for Fourier single-pixel imaging via diffusion model. Opt. Express, 32, 3138-3156(2024). https://doi.org/10.1364/OE.510692

    [43] Z. Zhang et al. Simultaneous spatial, spectral, and 3D compressive imaging via efficient Fourier single-pixel measurements. Optica, 5, 315-319(2018). https://doi.org/10.1364/OPTICA.5.000315

    [44] G. Qu et al. A demosaicing method for compressive color single-pixel imaging based on a generative adversarial network. Opt. Lasers Eng., 155, 107053(2022). https://doi.org/10.1016/j.optlaseng.2022.107053

    [45] J. Song, C. Meng, S. Ermon. Denoising diffusion implicit models(2020).

    [46] C. Li. An Efficient Algorithm for Total Variation Regularization with Applications to the Single Pixel Camera and Compressive Sensing(2010).

    [47] W.-K. Yu. Super sub-nyquist single-pixel imaging by means of Cake-cutting Hadamard basis sort. Sensors, 19, 4122(2019). https://doi.org/10.3390/s19194122

    [48] P. Dhariwal, A. Nichol. Diffusion models beat GANs on image synthesis, 8780-8794(2021).

    [49] H. S. Malvar, L.-W. He, R. Cutler. High-quality linear interpolation for demosaicing of Bayer-patterned color images, iii-485(2004).

    [50] P. G. Vaz et al. Image quality of compressive single-pixel imaging using different Hadamard orderings. Opt. Express, 28, 11666-11681(2020). https://doi.org/10.1364/OE.387612

    [51] Y. Tian, Y. Fu, J. Zhang. Plug-and-play algorithms for single-pixel imaging. Opt. Lasers Eng., 154, 106970(2022). https://doi.org/10.1016/j.optlaseng.2022.106970

    [52] Z. Pan et al. DiffSCI: zero-shot snapshot compressive imaging via iterative spectral diffusion model, 25297-25306(2024). https://doi.org/10.1109/CVPR52733.2024.02390

    [53] N. Antipa et al. Diffusercam: lensless single-exposure 3D imaging. Optica, 5, 1-9(2017). https://doi.org/10.1364/OPTICA.5.000001FF

    [54] D. Wu et al. Imaging biological tissue with high-throughput single-pixel compressive holography. Nat. Commun., 12, 4712(2021). https://doi.org/10.1038/s41467-021-24990-0

    [55] X. Yuan, D. J. Brady, A. K. Katsaggelos. Snapshot compressive imaging: theory, algorithms, and applications. IEEE Signal Process Mag., 38, 65-88(2021). https://doi.org/10.1109/MSP.2020.3023869

    Yifei Zhang, Yingxin Li, Zonghao Liu, Fei Wang, Guohai Situ, Mu Ku Chen, Haoqiang Wang, Zihan Geng, "Physics and data-driven alternative optimization enabled ultra-low-sampling single-pixel imaging," Adv. Photon. Nexus 4, 036005 (2025)
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