• Advanced Photonics
  • Vol. 5, Issue 6, 066003 (2023)
Xin Tong1、2, Renjun Xu2, Pengfei Xu1, Zishuai Zeng1, Shuxi Liu1, and Daomu Zhao1、*
Author Affiliations
  • 1Zhejiang University, School of Physics, Zhejiang Province Key Laboratory of Quantum Technology and Device, Hangzhou, China
  • 2Zhejiang University, Center for Data Science, Hangzhou, China
  • show less
    DOI: 10.1117/1.AP.5.6.066003 Cite this Article Set citation alerts
    Xin Tong, Renjun Xu, Pengfei Xu, Zishuai Zeng, Shuxi Liu, Daomu Zhao. Harnessing the magic of light: spatial coherence instructed swin transformer for universal holographic imaging[J]. Advanced Photonics, 2023, 5(6): 066003 Copy Citation Text show less
    References

    [1] L. B. Lesem, P. M. Hirsch, J. A. Jordan. Scientific applications: computer synthesis of holograms for 3D display. Commun. ACM, 11, 661-674(1968).

    [2] M. Lurie. Fourier-transform holograms with partially coherent light: holographic measurement of spatial coherence. J. Opt. Soc. Am., 58, 614-619(1968).

    [3] U. Schnars, W. Jüptner. Direct recording of holograms by a CCD target and numerical reconstruction. Appl. Opt., 33, 179-181(1994).

    [4] R. Horisaki et al. Compressive propagation with coherence. Opt. Lett., 47, 613-616(2022).

    [5] D. Blinder et al. Signal processing challenges for digital holographic video display systems. Signal Process. Image Commun., 70, 114-130(2019).

    [6] H. Ko, H. Y. Kim. Deep learning-based compression for phase-only hologram. IEEE Access, 9, 79735-79751(2021).

    [7] L. Shi et al. Towards real-time photorealistic 3D holography with deep neural networks. Nature, 591, 234-239(2021).

    [8] C. Lee et al. Deep learning based on parameterized physical forward model for adaptive holographic imaging with unpaired data. Nat. Mach. Intell., 5, 35-45(2023).

    [9] X. Guo et al. Stokes meta-hologram toward optical cryptography. Nat. Commun., 13, 6687(2022).

    [10] H. Yang et al. Angular momentum holography via a minimalist metasurface for optical nested encryption. Light Sci. Appl., 12, 79(2023).

    [11] R. Fiolka, K. Si, M. Cui. Complex wavefront corrections for deep tissue focusing using low coherence backscattered light. Opt. Express, 20, 16532-16543(2012).

    [12] S. Lim et al. Optimal spatial coherence of a light-emitting diode in a digital holographic display. Appl. Sci., 12, 4176(2022).

    [13] Y. Deng, D. Chu. Coherence properties of different light sources and their effect on the image sharpness and speckle of holographic displays. Sci. Rep., 7, 5893(2017).

    [14] X. Tong et al. A deep-learning approach for low-spatial-coherence imaging in computer-generated holography. Adv. Photonics Res., 4, 2200264(2023).

    [15] Y. Peng et al. Speckle-free holography with partially coherent light sources and camera-in-the-loop calibration. Sci. Adv., 7, 5040(2021).

    [16] F. Wang et al. Propagation of coherence-OAM matrix of an optical beam in vacuum and turbulence. Opt. Express, 31, 20796-20811(2023).

    [17] D. Jin et al. Neutralizing the impact of atmospheric turbulence on complex scene imaging via deep learning. Nat. Mach. Intell., 3, 876-884(2021).

    [18] Q. Zhang et al. Effect of oceanic turbulence on the visibility of underwater ghost imaging. J. Opt. Soc. Am. A, 36, 397-402(2019).

    [19] K. Wang et al. Deep learning wavefront sensing and aberration correction in atmospheric turbulence. PhotoniX, 2, 8(2021).

    [20] Y. Chen et al. A wavelet based deep learning method for underwater image super resolution reconstruction. IEEE Access, 8, 117759-117769(2020).

    [21] L. Zhang et al. Restoration of single pixel imaging in atmospheric turbulence by Fourier filter and CGAN. Appl. Phys. B, 127, 45(2021).

    [22] Y. Baykal, Y. Ata, M. C. Gökçe. Underwater turbulence, its effects on optical wireless communication and imaging: a review. Opt. Laser Technol., 156, 108624(2022).

    [23] J. Bertolotti, O. Katz. Imaging in complex media. Nat. Phys., 18, 1008-1017(2022).

    [24] T. Zeng, Y. Zhu, E. Y. Lam. Deep learning for digital holography: a review. Opt. Express, 29, 40572-40593(2021).

    [25] A. Khan et al. GAN-Holo: generative adversarial networks-based generated holography using deep learning. Complexity, 2021, 6662161(2021).

    [26] M. Liao et al. Scattering imaging as a noise removal in digital holography by using deep learning. New J. Phys., 24, 083014(2022).

    [27] T. Shimobaba et al. Deep-learning computational holography: a review. Front. Photonics, 3, 854391(2022).

    [28] Y. Rivenson, Y. Wu, A. Ozcan. Deep learning in holography and coherent imaging. Light Sci. Appl., 8, 85(2019).

    [29] Z. Chen et al. Physics-driven deep learning enables temporal compressive coherent diffraction imaging. Optica, 9, 677(2022).

    [30] Y. Jo et al. Holographic deep learning for rapid optical screening of anthrax spores. Sci. Adv., 3, e1700606(2023).

    [31] K. He et al. Deep residual learning for image recognition, 770-778(2016).

    [32] Z. Liu et al. Swin transformer: hierarchical vision transformer using shifted windows, 10012-10022(2021).

    [33] V. V. Nikishov. Spectrum of turbulent fluctuations of the sea-water refraction index. Int. J. Fluid Mech. Res., 27, 82-98(2000).

    [34] B. E. Stribling, B. M. Welsh, M. C. Roggemann. Optical propagation in non-Kolmogorov atmospheric turbulence. Proc. SPIE, 2471, 181-195(1995).

    [35] R. W. Gerchberg. A practical algorithm for the determination of phase from image and diffraction plane pictures. Optik, 35, 237-246(1972).

    [36] D. Martin et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, 416-423(2001).

    [37] Z. Liu et al. Deep learning face attributes in the wild, 3730-3738(2015).

    [38] P. Young et al. From image descriptions to visual denotations: new similarity metrics for semantic inference over event descriptions. Trans. Assoc. Comput. Linguist., 2, 67-78(2014).

    [39] W. Li et al. WebVision database: visual learning and understanding from web data(2017).

    [40] E. Agustsson, R. Timofte. NTIRE 2017 challenge on single image super-resolution: dataset and study, 1122-1131(2017).

    [41] I. Loshchilov, F. Hutter. Decoupled weight decay regularization(2017).

    [42] O. Ronneberger, P. Fischer, T. Brox. U-Net: convolutional networks for biomedical image segmentation. Lect. Notes Comput. Sci., 9351, 234-241(2015).

    [43] A. Kirillov et al. Segment anything(2023).

    [44] F. Gori, M. Santarsiero. Devising genuine spatial correlation functions. Opt. Lett., 32, 3531-3533(2007).

    Xin Tong, Renjun Xu, Pengfei Xu, Zishuai Zeng, Shuxi Liu, Daomu Zhao. Harnessing the magic of light: spatial coherence instructed swin transformer for universal holographic imaging[J]. Advanced Photonics, 2023, 5(6): 066003
    Download Citation