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Photonics Research, Volume. 11, Issue 4, 631(2023)

Learning-based super-resolution interpolation for sub-Nyquist sampled laser speckles

Huanhao Li1,2、†, Zhipeng Yu1,2、†, Qi Zhao1,2、†, Yunqi Luo3, Shengfu Cheng1,2, Tianting Zhong1,2, Chi Man Woo1,2, Honglin Liu1,4, Lihong V. Wang5,7、*, Yuanjin Zheng3,8、*, and Puxiang Lai1,2,6,9、*
Author Affiliations
  • 1Department of Biomedical Engineering, Hong Kong Polytechnic University, Hong Kong, China
  • 2Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen 518063, China
  • 3School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore 639798, Singapore
  • 4Key Laboratory for Quantum Optics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
  • 5Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, California 91125, USA
  • 6Photonics Research Institute, Hong Kong Polytechnic University, Hong Kong, China
  • 7e-mail: LVW@caltech.edu
  • 8e-mail: yjzheng@ntu.edu.sg
  • 9e-mail: puxiang.lai@polyu.edu.hk
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    [1] J. W. Goodman. Speckle Phenomena in Optics: Theory and Applications(2015).

    [2] I. M. Vellekoop, A. Mosk. Focusing coherent light through opaque strongly scattering media. Opt. Lett., 32, 2309-2311(2007).

    [3] S. Popoff, G. Lerosey, R. Carminati, M. Fink, A. Boccara, S. Gigan. Measuring the transmission matrix in optics: an approach to the study and control of light propagation in disordered media. Phys. Rev. Lett., 104, 100601(2010).

    [4] P. Lai, L. Wang, J. W. Tay, L. V. Wang. Photoacoustically guided wavefront shaping (PAWS) for enhanced optical focusing in scattering media. Nat. Photonics, 9, 126-132(2015).

    [5] J. Xu, H. W. Ruan, Y. Liu, H. J. Zhou, C. H. Yang. Focusing light through scattering media by transmission matrix inversion. Opt. Express, 25, 27234-27246(2017).

    [6] J.-H. Park, Z. Yu, K. Lee, P. Lai, Y. Park. Perspective: wavefront shaping techniques for controlling multiple light scattering in biological tissues: toward in vivo applications. APL Photonics, 3, 100901(2018).

    [7] H. Li, C. M. Woo, T. Zhong, Z. Yu, Y. Luo, Y. Zheng, X. Yang, H. Hui, P. Lai. Adaptive optical focusing through perturbed scattering media with a dynamic mutation algorithm. Photonics Res., 9, 202-212(2021).

    [8] Y. Luo, S. Yan, H. Li, P. Lai, Y. Zheng. Towards smart optical focusing: deep learning-empowered dynamic wavefront shaping through nonstationary scattering media. Photonics Res., 9, B262-B278(2021).

    [9] Z. Yu, H. Li, T. Zhong, J.-H. Park, S. Cheng, C. M. Woo, Q. Zhao, J. Yao, Y. Zhou, X. Huang, W. Pang, H. Yoon, Y. Shen, H. Liu, Y. Zheng, Y. Park, L. V. Wang, P. Lai. Wavefront shaping: a versatile tool to conquer multiple scattering in multidisciplinary fields. Innovation, 3, 100292(2022).

    [10] O. Katz, P. Heidmann, M. Fink, S. Gigan. Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations. Nat. Photonics, 8, 784-790(2014).

    [11] T. Wu, O. Katz, X. Shao, S. Gigan. Single-shot diffraction-limited imaging through scattering layers via bispectrum analysis. Opt. Lett., 41, 5003-5006(2016).

    [12] J. Bertolotti, E. G. van Putten, C. Blum, A. Lagendijk, W. L. Vos, A. P. Mosk. Non-invasive imaging through opaque scattering layers. Nature, 491, 232-234(2012).

    [13] H. Yilmaz, E. G. van Putten, J. Bertolotti, A. Lagendijk, W. L. Vos, A. P. Mosk. Speckle correlation resolution enhancement of wide-field fluorescence imaging. Optica, 2, 424-429(2015).

    [14] Y. Xu, X. Liu, X. Cao, C. Huang, E. Liu, S. Qian, X. Liu, Y. Wu, F. Dong, C. W. Qiu, J. Qiu, K. Hua, W. Su, J. Wu, H. Xu, Y. Han, C. Fu, Z. Yin, M. Liu, R. Roepman, S. Dietmann, M. Virta, F. Kengara, Z. Zhang, L. Zhang, T. Zhao, J. Dai, J. Yang, L. Lan, M. Luo, Z. Liu, T. An, B. Zhang, X. He, S. Cong, X. Liu, W. Zhang, J. P. Lewis, J. M. Tiedje, Q. Wang, Z. An, F. Wang, L. Zhang, T. Huang, C. Lu, Z. Cai, F. Wang, J. Zhang. Artificial intelligence: a powerful paradigm for scientific research. Innovation, 2, 100179(2021).

    [15] H. Li, Z. Yu, Q. Zhao, T. Zhong, P. Lai. Accelerating deep learning with high energy efficiency: from microchip to physical systems. Innovation, 3, 100252(2022).

    [16] S. Li, M. Deng, J. Lee, A. Sinha, G. Barbastathis. Imaging through glass diffusers using densely connected convolutional networks. Optica, 5, 803-813(2018).

    [17] N. Borhani, E. Kakkava, C. Moser, D. Psaltis. Learning to see through multimode fibers. Optica, 5, 960-966(2018).

    [18] B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, C. Moser. Multimode optical fiber transmission with a deep learning network. Light Sci. Appl., 7, 69(2018).

    [19] Y. Li, Y. Xue, L. Tian. Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media. Optica, 5, 1181-1190(2018).

    [20] P. Caramazza, O. Moran, R. Murray-Smith, D. Faccio. Transmission of natural scene images through a multimode fibre. Nat. Commun., 10, 2029(2019).

    [21] Q. Zhao, H. Li, Z. Yu, C. M. Woo, T. Zhong, S. Cheng, Y. Zheng, H. Liu, J. Tian, P. Lai. Speckle-based optical cryptosystem and its application for human face recognition via deep learning. Adv. Sci., 9, e2202407(2022).

    [22] S. Zhu, E. Guo, J. Gu, L. Bai, J. Han. Imaging through unknown scattering media based on physics-informed learning. Photonics Res., 9, B210-B219(2021).

    [23] M. Liao, S. Zheng, S. Pan, D. Lu, W. He, G. Situ, X. Peng. Deep-learning-based ciphertext-only attack on optical double random phase encryption. Opto-Electron. Adv., 4, 200016(2021).

    [24] Z. Zhou, J. Xia, J. Wu, C. Chang, X. Ye, S. Li, B. Du, H. Zhang, G. Tong. Learning-based phase imaging using a low-bit-depth pattern. Photonics Res., 8, 1624-1633(2020).

    [25] M. Lyu, H. Wang, G. Li, S. Zheng, G. Situ. Learning-based lensless imaging through optically thick scattering media. Adv. Photonics, 1, 036002(2019).

    [26] S. Popoff, G. Lerosey, M. Fink, A. C. Boccara, S. Gigan. Image transmission through an opaque material. Nat. Commun., 1, 81(2010).

    [27] C.-Y. Yang, C. Ma, M.-H. Yang. Single-image super-resolution: a benchmark. European Conference on Computer Vision (ECCV), 372-386(2014).

    [28] Z. Wang, J. Chen, S. C. H. Hoi. Deep Learning for Image Super-Resolution: A Survey(2020).

    [29] H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Gunaydin, L. A. Bentolila, C. Kural, A. Ozcan. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat. Methods, 16, 103-110(2019).

    [30] Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, A. Ozcan. Deep learning microscopy. Optica, 4, 1437-1443(2017).

    [31] J. W. Goodman. Statistical Optics(2015).

    [32] S. Xie, R. Girshick, P. Dollár, Z. Tu, K. He. Aggregated residual transformations for deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1492-1500(2017).

    [33] W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, Z. Wang. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1874-1883(2016).

    [34] W. S. Lai, J. B. Huang, N. Ahuja, M. H. Yang. Fast and accurate image super-resolution with deep Laplacian Pyramid networks. IEEE Trans. Pattern Anal. Mach. Intell., 41, 2599-2613(2019).

    [35] M. Pascucci, S. Ganesan, A. Tripathi, O. Katz, V. Emiliani, M. Guillon. Compressive three-dimensional super-resolution microscopy with speckle-saturated fluorescence excitation. Nat. Commun., 10, 1327(2019).

    [36] W. Yang, X. Zhang, Y. Tian, W. Wang, J.-H. Xue, Q. Liao. Deep learning for single image super-resolution: a brief review. IEEE Trans. Multimedia, 21, 3106-3121(2019).

    [37] S. Cheng, Y. Zhou, J. Chen, H. Li, L. Wang, P. Lai. High-resolution photoacoustic microscopy with deep penetration through learning. Photoacoustics, 25, 100314(2022).

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    Huanhao Li, Zhipeng Yu, Qi Zhao, Yunqi Luo, Shengfu Cheng, Tianting Zhong, Chi Man Woo, Honglin Liu, Lihong V. Wang, Yuanjin Zheng, Puxiang Lai, "Learning-based super-resolution interpolation for sub-Nyquist sampled laser speckles," Photonics Res. 11, 631 (2023)

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    Paper Information

    Category: Image Processing and Image Analysis

    Received: Aug. 15, 2022

    Accepted: Nov. 6, 2022

    Published Online: Mar. 29, 2023

    The Author Email: Lihong V. Wang (LVW@caltech.edu), Yuanjin Zheng (yjzheng@ntu.edu.sg), Puxiang Lai (puxiang.lai@polyu.edu.hk)

    DOI:10.1364/PRJ.472512

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology