• Advanced Photonics Nexus
  • Vol. 2, Issue 4, 046006 (2023)
Xuyang Chang1、2, Rifa Zhao1、2, Shaowei Jiang3, Cheng Shen4, Guoan Zheng5, Changhuei Yang4, and Liheng Bian1、2、6、*
Author Affiliations
  • 1Beijing Institute of Technology, MIIT Key Laboratory of Complex-Field Intelligent Sensing, Beijing, China
  • 2Beijing Institute of Technology, School of Information and Electronics and Advanced Research Institute of Multidisciplinary Sciences, Beijing, China
  • 3Hangzhou Dianzi University, School of Communication Engineering, Hangzhou, China
  • 4California Institute of Technology, Department of Electrical Engineering, Pasadena, California, United States
  • 5University of Connecticut, Department of Biomedical Engineering, Storrs, Connecticut, United States
  • 6Yangtze Delta Region Academy of Beijing Institute of Technology (Jiaxing), Jiaxing, China
  • show less
    DOI: 10.1117/1.APN.2.4.046006 Cite this Article Set citation alerts
    Xuyang Chang, Rifa Zhao, Shaowei Jiang, Cheng Shen, Guoan Zheng, Changhuei Yang, Liheng Bian. Complex-domain-enhancing neural network for large-scale coherent imaging[J]. Advanced Photonics Nexus, 2023, 2(4): 046006 Copy Citation Text show less
    References

    [1] D. J. Brady et al. Multiscale gigapixel photography. Nature, 486, 386-389(2012).

    [2] X. Lin et al. All-optical machine learning using diffractive deep neural networks. Science, 361, 1004-1008(2018).

    [3] J. Li et al. Transport of intensity diffraction tomography with non-interferometric synthetic aperture for three-dimensional label-free microscopy. Light-Sci. Appl., 11, 154(2022).

    [4] Y. Xue et al. Single-shot 3D wide-field fluorescence imaging with a computational miniature mesoscope. Sci. Adv., 6, eabb7508(2020).

    [5] H. Pinkard et al. Learned adaptive multiphoton illumination microscopy for large-scale immune response imaging. Nat. Commun., 12, 1916(2021).

    [6] J. Park et al. Review of bio-optical imaging systems with a high space-bandwidth product. Adv. Photonics, 3, 044001(2021).

    [7] J. Fan et al. Video-rate imaging of biological dynamics at centimetre scale and micrometre resolution. Nat. Photonics, 13, 809-816(2019).

    [8] G. Zheng, R. Horstmeyer, C. Yang. Wide-field, high-resolution Fourier ptychographic microscopy. Nat. Photonics, 7, 739-745(2013).

    [9] G. Zheng et al. Concept, implementations and applications of Fourier ptychography. Nat. Rev. Phys., 3, 207-223(2021).

    [10] O. Kulce et al. All-optical information-processing capacity of diffractive surfaces. Light-Sci. Appl., 10, 25(2021).

    [11] Y. Park, C. Depeursinge, G. Popescu. Quantitative phase imaging in biomedicine. Nat. Photonics, 12, 578-589(2018).

    [12] Y. Rivenson et al. Phase recovery and holographic image reconstruction using deep learning in neural networks. Light-Sci. Appl., 7, 17141-17141(2018).

    [13] Y. Rivenson et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nat. Biomed. Eng., 3, 466-477(2019).

    [14] S. Cheng et al. Single-cell cytometry via multiplexed fluorescence prediction by label-free reflectance microscopy. Sci. Adv., 7, eabe0431(2021).

    [15] C. Zuo et al. Deep learning in optical metrology: a review. Light-Sci. Appl., 11, 39(2022).

    [16] W. Luo et al. Synthetic aperture-based on-chip microscopy. Light-Sci. Appl., 4, e261-e261(2015).

    [17] W. Luo et al. Pixel super-resolution using wavelength scanning. Light-Sci. Appl., 5, e16060-e16060(2016).

    [18] Y. Gao, L. Cao. Generalized optimization framework for pixel super-resolution imaging in digital holography. Opt. Express, 29, 28805-28823(2021).

    [19] S. Jiang et al. Resolution-enhanced parallel coded ptychography for high-throughput optical imaging. ACS Photonics, 8, 3261-3271(2021).

    [20] X. Chang et al. Plug-and-play pixel super-resolution phase retrieval for digital holography. Opt. Lett., 47, 2658-2661(2022).

    [21] M. Elad, M. Aharon. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image process., 15, 3736-3745(2006).

    [22] X. Lan et al. Efficient belief propagation with learned higher-order Markov random fields. Lect. Notes Comput. Sci., 3952, 269-282(2006).

    [23] Y. Weiss, W. T. Freeman. What makes a good model of natural images?, 1-8(2007).

    [24] K. Dabov et al. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image process., 16, 2080-2095(2007).

    [25] V. Katkovnik, K. Egiazarian. Sparse phase imaging based on complex domain nonlocal BM3D techniques. Digital Signal Process., 63, 72-85(2017).

    [26] K. Zhang et al. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image process., 26, 3142-3155(2017).

    [27] K. Zhang, W. Zuo, L. Zhang. FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image process., 27, 4608-4622(2018).

    [28] D. P. Reichert, T. Serre. Neuronal synchrony in complex-valued deep networks(2013).

    [29] G. Shi, M. M. Shanechi, P. Aarabi. On the importance of phase in human speech recognition. IEEE-ACM Trans. Audio Speech Lang. Process., 14, 1867-1874(2006).

    [30] C. Trabelsi et al. Deep complex networks(2017).

    [31] Y. Gao, L. Cao. A complex constrained total variation image denoising algorithm with application to phase retrieval(2021).

    [32] S. H. Chan, X. Wang, O. A. Elgendy. Plug-and-play ADMM for image restoration: fixed-point convergence and applications. IEEE Trans. Comput. Imaging, 3, 84-98(2016).

    [33] X. Chang, L. Bian, J. Zhang. Large-scale phase retrieval. eLight, 1, 4(2021).

    [34] S. Skylaki, O. Hilsenbeck, T. Schroeder. Challenges in long-term imaging and quantification of single-cell dynamics. Nat. Biotechnol., 34, 1137-1144(2016).

    [35] F. Zhang et al. Physics-based iterative projection complex neural network for phase retrieval in lensless microscopy imaging, 10523-10531(2021).

    [36] Y. Baek et al. Kramers–Kronig holographic imaging for high-space-bandwidth product. Optica, 6, 45-51(2019).

    [37] Y. Baek, Y. Park. Intensity-based holographic imaging via space-domain Kramers–Kronig relations. Nat. Photonics, 15, 354-360(2021).

    [38] C. Shen et al. Non-iterative complex wave-field reconstruction based on Kramers–Kronig relations. Photonics Res., 9, 1003-1012(2021).

    [39] S. Jiang et al. Wide-field, high-resolution lensless on-chip microscopy via near-field blind ptychographic modulation. Lab Chip, 20, 1058-1065(2020).

    [40] S. Jiang et al. High-throughput digital pathology via a handheld, multiplexed, and AI-powered ptychographic whole slide scanner. Lab Chip, 22, 2657-2670(2022).

    [41] S. Jiang et al. Blood-coated sensor for high-throughput ptychographic cytometry on a Blu-ray disc. ACS Sens., 7, 1058-1067(2022).

    [42] R. E. Carlson, F. N. Fritsch. Monotone piecewise bicubic interpolation. SIAM J. Numer. Anal., 22, 386-400(1985).

    [43] J. R. Fienup. Phase retrieval algorithms: a comparison. Appl. Opt., 21, 2758-2769(1982).

    [44] A. M. Maiden, J. M. Rodenburg. An improved ptychographical phase retrieval algorithm for diffractive imaging. Ultramicroscopy, 109, 1256-1262(2009).

    [45] T. Falk et al. U-Net: deep learning for cell counting, detection, and morphometry. Nat. Methods, 16, 67-70(2019).

    [46] X. Zheng et al. Fast and robust segmentation of white blood cell images by self-supervised learning. Micron, 107, 55-71(2018).

    [47] K. de Haan et al. Deep learning-based transformation of H&E stained tissues into special stains. Nat. Commun., 12, 4884(2021).

    [48] S. Guo et al. Toward convolutional blind denoising of real photographs, 1712-1722(2019).

    [49] K. Wei et al. TFPnP: tuning-free plug-and-play proximal algorithms with applications to inverse imaging problems. J. Mach. Learn. Res., 23, 1-48(2022).

    [50] F. Wang et al. Phase imaging with an untrained neural network. Light-Sci. Appl., 9, 77(2020).

    [51] J. R. Hershey, J. L. Roux, F. Weninger. Deep unfolding: model-based inspiration of novel deep architectures(2014).

    [52] B. Zhang et al. End-to-end snapshot compressed super-resolution imaging with deep optics. Optica, 9, 451-454(2022).

    Xuyang Chang, Rifa Zhao, Shaowei Jiang, Cheng Shen, Guoan Zheng, Changhuei Yang, Liheng Bian. Complex-domain-enhancing neural network for large-scale coherent imaging[J]. Advanced Photonics Nexus, 2023, 2(4): 046006
    Download Citation