A data-driven Ghost Imaging System with transmitter-end wavefront correction

doi:10.7523/j.ucas.2026.038

Abstract

Abstract: Ghost imaging (GI) technology holds significant application potential in fields such as biomedicine, remote sensing, and underwater communication. Based on the statistical characteristics of second-order correlation imaging, this technology exhibits strong robustness against scattering interference in the receiving path. However, when wavefront scattering distortion occurs in the illumination path, the speckle correlation is severely degraded, leading to a drastic decline in imaging quality. Existing solutions mostly rely on manually designed projection patterns or complex physical modeling. These methods suffer from weak generalization ability and low efficiency, making it difficult to cope with diverse static distortion environments. To address this, this study proposes a wavefront pre-compensation method for ghost imaging under severe scattering interference and designs a ghost imaging system based on closed-loop feedback of transmitter-end wavefront optimization. By introducing deep learning techniques, the system incorporates a Feedback-Regulated Speckle Distortion Network (FRSD-Net). Through a closed-loop feedback mechanism, it intelligently generates optimal illumination speckles, effectively ensuring that the light field maintains a high signal-to-noise ratio (SNR) and strong correlation characteristics even after penetrating severely inhomogeneous media. Experimental results demonstrate that the proposed system significantly improves ghost imaging quality under severe distortion scenarios, such as those caused by water stains and polymers. The PSNR/SSIM of the reconstructed images increased from 6.45/0.12 to 22.00/0.73, and from 15.71/0.32 to 23.19/0.68, respectively. This approach effectively combines the high-speed modulation advantages of Digital Micromirror Devices (DMD) with the modeling capabilities of deep learning, providing a new pathway for the practical application of ghost imaging in complex scattering environments.

Key words: Ghost imaging, Wavefront shaping, Digital micromirror device, Deep learning, Scattering compensation

CLC Number:

O436

Bowen Chu, Jiakui Tang, Sen Lang, Fang Yan. A data-driven Ghost Imaging System with transmitter-end wavefront correction[J]. Journal of University of Chinese Academy of Sciences, DOI: 10.7523/j.ucas.2026.038.

References

[1] Ntziachristos V.Going deeper than microscopy: The optical imaging frontier in biology[J]. Nature Methods, 2010, 7(8): 603-614. DOI:10.1038/nmeth.1483.
[2] Gigan S.Optical microscopy aims deep[J]. Nature Photonics, 2017, 11(1): 14-16. DOI:10.1038/nphoton.2016.257.
[3] Bertolotti J, van Putten E G, Blum C, et al. Non-invasive imaging through opaque scattering layers[J]. Nature, 2012, 491(7423): 232-234. DOI:10.1038/nature11578.
[4] Katz O, Heidmann P, Fink M, et al.Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations[J]. Nature Photonics, 2014, 8(10): 784-790. DOI:10.1038/nphoton.2014.189.
[5] Rotter S, Gigan S.Light fields in complex media: Mesoscopic scattering meets wave control[J]. Reviews of Modern Physics, 2017, 89: 015005. DOI:10.1103/revmodphys.89.015005.
[6] Mosk A P, Lagendijk A, Lerosey G, et al.Controlling waves in space and time for imaging and focusing in complex media[J]. Nature Photonics, 2012, 6(5): 283-292. DOI:10.1038/nphoton.2012.88.
[7] Popoff S M, Lerosey G, Carminati R, et al.Measuring the transmission matrix in optics: An approach to the study and control of light propagation in disordered media[J]. Physical Review Letters, 2010, 104(10): 100601. DOI:10.1103/physrevlett.104.100601.
[8] Horstmeyer R, Ruan H W, Yang C.Guidestar-assisted wavefront-shaping methods for focusing light into biological tissue[J]. Nature Photonics, 2015, 9(9): 563-571. DOI:10.1038/nphoton.2015.140.
[9] Pittman T B, Shih Y H, Strekalov D V, et al.Optical imaging by means of two-photon quantum entanglement[J]. Physical Review A, 1995, 52(5): R3429-R3432. DOI:10.1103/physreva.52.r3429.
[10] Shapiro J H.Computational ghost imaging[J]. Physical Review A, 2008, 78(6): 061802. DOI:10.1103/physreva.78.061802.
[11] Katz O, Bromberg Y, Silberberg Y.Compressive ghost imaging[J]. Applied Physics Letters, 2009, 95(13): 131110. DOI:10.1063/1.3238296.
[12] Erkmen B I, Shapiro J H.Ghost imaging: From quantum to classical to computational[J]. Advances in Optics and Photonics, 2010, 2(4): 405-450.
[13] Gibson G M, Johnson S D, Padgett M J.Single-pixel imaging 12 years on: A review[J]. Optics Express, 2020, 28(19): 28190. DOI:10.1364/oe.403195.
[14] Meyers R E, Deacon K S, Shih Y.Turbulence-free ghost imaging[J]. Applied Physics Letters, 2011, 98(11): 111115. DOI:10.1063/1.3567931.
[15] Tajahuerce E, Durán V, Clemente P, et al.Image transmission through dynamic scattering media by single-pixel photodetection[J]. Optics Express, 2014, 22(14): 16945. DOI:10.1364/oe.22.016945.
[16] Vellekoop I M, Mosk A P.Focusing coherent light through opaque strongly scattering media[J]. Optics Letters, 2007, 32(16): 2309. DOI:10.1364/ol.32.002309.
[17] Vellekoop I M.Feedback-based wavefront shaping[J]. Optics Express, 2015, 23(9): 12189. DOI:10.1364/oe.23.012189.
[18] Popoff S, Lerosey G, Fink M, et al.Image transmission through an opaque material[J]. Nature Communications, 2010, 1: 81. DOI:10.1038/ncomms1078.
[19] Judkewitz B, Wang Y M, Horstmeyer R, et al.Speckle-scale focusing in the diffusive regime with time reversal of variance-encoded light (TROVE)[J]. Nature Photonics, 2013, 7(4): 300-305. DOI:10.1038/nphoton.2013.31.
[20] Park J H, Yu Z P, Lee K, et al.Perspective: Wavefront shaping techniques for controlling multiple light scattering in biological tissues: Toward in vivo applications[J]. APL Photonics, 2018, 3(10): 100901. DOI:10.1063/1.5033917.
[21] Goorden S A, Bertolotti J, Mosk A P.Superpixel-based spatial amplitude and phase modulation using a digital micromirror device[J]. Optics Express, 2014, 22(15): 17999-18009. DOI:10.1364/OE.22.017999.
[22] Sun B, Edgar M P, Bowman R, et al.3D computational imaging with single-pixel detectors[J]. Science, 2013, 340(6134): 844-847. DOI:10.1126/science.1234454.
[23] Chen Y F, An H J, Sun Z, et al. Large model enhanced computational ghost imaging[EB/OL]. 2025: arXiv: 2503.08710. https://arxiv.org/abs/2503.08710
[24] Huang Y Y, Loesel P D, Paganin D M, et al. Deep learning in classical X-ray ghost imaging for dose reduction[EB/OL]. 2024: arXiv: 2411.06340. https://arxiv.org/abs/2411.06340
[25] Booth M J.Adaptive optical microscopy: The ongoing quest for a perfect image[J]. Light: Science & Applications, 2014, 3(4): e165. DOI:10.1038/lsa.2014.46.
[26] Ji N.Adaptive optical fluorescence microscopy[J]. Nature Methods, 2017, 14(4): 374-380. DOI:10.1038/nmeth.4218.
[27] Li Y G, Wang J J, Xiao T L, et al. Real-time imaging through dynamic scattering media enabled by fixed optical modulations[EB/OL]. 2025: arXiv: 2508.15286. https://arxiv.org/abs/2508.15286
[28] Du Y T, Liu Z C, Huang Y, et al. Fast physics-driven untrained network for highly nonlinear inverse scattering problems[EB/OL]. 2026: arXiv: 2602.13805. https://arxiv.org/abs/2602.13805
[29] Li Y G, Zheng Z, Wang J J, et al. Optical diffraction neural networks assisted computational ghost imaging through dynamic scattering media[EB/OL]. 2025: arXiv: 2511.22913. https://arxiv.org/abs/2511.22913
[30] González H, Martínez-León L, Soldevila F, et al. High sampling rate single-pixel digital holography system employing a DMD and phase-encoded patterns[EB/OL]. 2024: arXiv: 2402.03786. https://arxiv.org/abs/2402.03786
[31] Lee K, Park Y.Exploiting the speckle-correlation scattering matrix for a compact reference-free holographic image sensor[J]. Nature Communications, 2016, 7: 13359. DOI:10.1038/ncomms13359.
[32] Tzang O, Caravaca-Aguirre A M, Wagner K, et al. Adaptive wavefront shaping for controlling nonlinear multimode interactions in optical fibres[J]. Nature Photonics, 2018, 12(6): 368-374. DOI:10.1038/s41566-018-0167-7.
[33] Turtaev S, Leite I T, Mitchell K J, et al.Comparison of nematic liquid-crystal and DMD based spatial light modulation in complex photonics[J]. Optics Express, 2017, 25(24): 29874. DOI:10.1364/oe.25.029874.
[34] Barbastathis G, Ozcan A, Situ G H.On the use of deep learning for computational imaging[J]. Optica, 2019, 6(8): 921. DOI:10.1364/optica.6.000921.
[35] Manni M, Karpov D, Batenburg K J, et al. Noise2Ghost: Self-supervised deep convolutional reconstruction for ghost imaging[EB/OL]. 2025: arXiv: 2504.10288. https://arxiv.org/abs/2504.10288
[36] Tudosie S C, Gandolfi V, Varakkoth S, et al. Learned single-pixel fluorescence microscopy[EB/OL]. 2025: arXiv: 2507.18740. https://arxiv.org/abs/2507.18740
[37] Ferri F, Magatti D, Gatti A, et al.High-resolution ghost image and ghost diffraction experiments with thermal light[J]. Physical Review Letters, 2005, 94(18): 183602. DOI:10.1103/physrevlett.94.183602.
[38] Valencia A, Scarcelli G, D’Angelo M, et al. Two-photon imaging with thermal light[J]. Physical Review Letters, 2005, 94(6): 063601. DOI:10.1103/PhysRevLett.94.063601.
[39] D’Angelo M, Shih Y H. Quantum imaging[J]. Laser Physics Letters, 2005, 2(12): 567-596. DOI:10.1002/lapl.200510054.
[40] Bromberg Y, Katz O, Silberberg Y.Ghost imaging with a single detector[J]. Physical Review A, 2009, 79(5): 053840. DOI:10.1103/physreva.79.053840.
[41] Gong W L, Han S S.High-resolution far-field ghost imaging via sparsity constraint[J]. Scientific Reports, 2015, 5: 9280. DOI:10.1038/srep09280.
[42] Bina M, Magatti D, Molteni M, et al.Backscattering differential ghost imaging in turbid media[J]. Physical Review Letters, 2013, 110(8): 083901. DOI:10.1103/physrevlett.110.083901.
[43] 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.
[44] Donoho D L.Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. DOI:10.1109/TIT.2006.871582.
[45] Erkmen B I, Shapiro J H.Signal-to-noise ratio of Gaussian-state ghost imaging[J]. Physical Review A, 2009, 79(2): 023833. DOI:10.1103/physreva.79.023833.