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ISSN: 1023-5086

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ISSN: 1023-5086

Scientific and technical

Opticheskii Zhurnal

A full-text English translation of the journal is published by Optica Publishing Group under the title “Journal of Optical Technology”

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DOI: 10.17586/1023-5086-2022-89-09-11-19

УДК: 535.8 004.93

Neural-network-enabled holographic image reconstruction via amplitude and phase extraction

For Russian citation (Opticheskii Zhurnal):
  

Рымов Д.А., Стариков Р.С., Черёмхин П.А. Нейросетевая реконструкция сцен с цифровых голограмм на основе извлечения амплитуды и фазы // Оптический журнал. 2022. Т. 89. № 9. С. 11–19. http://doi.org/ 10.17586/1023-5086-2022-89-09-11-19

 

Rymov D.A., Starikov R.S., Cheremkhin P.A. Neural-network-enabled holographic image reconstruction via amplitude and phase extraction [in Russian] // Opticheskii Zhurnal. 2022. V.89. № 9. P. 11-19. http://doi.org/ 10.17586/1023-5086-2022-89-09-11-19   

For citation (Journal of Optical Technology):

D. A. Rymov, R. S. Starikov, and P. A. Cheremkhin, "Neural-network-enabled holographic image reconstruction via amplitude and phase extraction," Journal of Optical Technology. 89(9), 511-516 (2022). https://doi.org/10.1364/JOT.89.000511

 

Abstract:

Subject of study. We present research on the range of atmospheric optical communications links as a function of atmospheric conditions, the types of laser beam modulation used, and the type of interference-resistant coding used. Aim. We develop an approach for increasing the range of free-space optical communications links by improving resistance to atmospheric turbulence. Methods. We show that the modulation technique and channel coding must be compatible if optimum results are to be achieved. Pulse position modulation and interference-resistant position coding are one example of such a compatible combination; a holographic code in which a sequence of pulses representing a 1D linear hologram of a single pulse replaces a single pulse in the desired position is one example of an interference-resistant code. In this case, position modulation is still being used, but in the form of multiple pulse position modulation. A holographic code takes advantage of the fact that holograms are divisible, and a fragment of the hologram can be used for recovery of transmitted data (as well as data hidden by noise). The recovery capacity of the holographic code depends on the hologram length—the number of slots per symbol interval. Main results. We show that holographic coding provides higher interference resistance and a lower probability of detector error and provides the same benefit as an increase in transmitter power. Practical significance. The benefit from holographic coding may be used to either increase the communications range or increase the communications channel reliability. Position coding has the advantages that the redundant information required for interference-resistant encoding is created within the symbol interval, does not affect the symbol repetition frequency, and has no effect on the data transmission rate.

Keywords:

atmospheric optical communication lines, position-pulse modulation, position noise-resistant coding

Acknowledgements:
the work was carried out with the financial support of the Russian Science Foundation, grant No. 20-79-00291

OCIS codes: 090.1995, 110.4280, 230.5160, 090.1760

References:

1. W. Ma, Z. Liu, Z. A. Kudyshev, A. Boltasseva, W. Cai, and Y. Liu, “Deep learning for the design of photonic structures,” Nat. Photonics 15(2), 77–90 (2021).

2. G. Genty, L. Salmela, J. M. Dudley, D. Brunner, A. Kokhanovskiy, S. Kobtsev, and S. K. Turitsyn, “Machine learning and applications in ultrafast photonics,” Nat. Photonics 15(2), 91–101 (2021).

3. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, “Inception v4, inception-ResNet and the impact of residual connections on learning,” in Proceedings of the 31st AAAI Conference on Artificial Intelligence (2017), pp. 4278–4284.

4. M. Yu. Alies, E. A. Antonov, A. I. Kalugin, and M. R. Zaripov, “Application of artificial neural networks for the analysis of multispectral images,” J. Opt. Technol. 88(8), 441–444 (2021) [Opt. Zh. 88(8), 48–53 (2021)].

5. L. Xie, F. Lee, L. Liu, K. Kotani, and Q. Chen, “Scene recognition: a comprehensive survey,” Pattern Recognit. 102, 107205 (2020).

6. T. Liu, K. de Haan, Y. Rivenson, Z. Wei, X. Zeng, Y. Zhang, and A. Ozcan, “Deep learning-based superresolution in coherent imaging systems,” Sci. Rep. 9, 3926 (2019).

7. S. Li, W. Zhu, B. Zhang, X. Yang, and M. Chen, “Depth map denoising using a bilateral filter and a progressive CNN,” J. Opt. Technol. 87(6), 361–364 (2020) [Opt. Zh. 87(6), 51–56 (2020)].

8. P. A. Cheremkhin, N. N. Evtikhiev, V. V. Krasnov, V. G. Rodin, D. A. Rymov, and R. S. Starikov, “Machine learning methods for digital holography and diffractive optics,” Procedia Comput. Sci. 169, 440–444 (2020).

9. E. M. Hossein, W. N. Caira, M. Atisa, P. Chakravarthula, and C. N. Pégard, “DeepCGH: 3D computer-generated holography using deep learning,” Opt. Express 28, 26636–26650 (2020).

10. T. Zeng, Y. Zhu, and E. Y. Lam, “Deep learning for digital holography: a review,” Opt. Express 29, 40572–40593 (2021).

11. K. Wang, J. Dou, Q. Kemao, J. Di, and J. Zhao, “Y-Net: a one-to-two deep learning framework for digital holographic reconstruction,” Opt. Lett. 44, 4765–4768 (2019).

12. H. Wang, M. Lyu, and G. Situ, “eHoloNet: a learning-based end-to-end approach for in-line digital holographic reconstruction,” Opt. Express 26, 22603–22614 (2018).

13. U. Schnars, C. Falldorf, J. Watson, and W. Jüptner, Digital Holography and Wavefront Sensing: Principles, Techniques and Applications (Springer-Verlag, Berlin, 2015).

14. S. Ioffe and Ch. Szegedy, “Batch normalization: accelerating deep network training by reducing internal covariate shift,” in Proceedings of the 32nd International Conference on Machine Learning (2015), pp. 448–456.

15. B. Ding, H. Qian, and J. Zhou, “Activation functions and their characteristics in deep neural networks,” in Chinese Control and Decision Conference (2018), pp. 1836–1841.

16. O. Ronneberger, T. Fischer, and T. Brox, “U-Net: convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted

Intervention (2015), pp. 234–241.

17. N. Verrier and M. Atlan, “Off-axis digital hologram reconstruction: some practical considerations,” Appl. Opt. 50, H136–H146 (2011).

18. N. Pavillon, C. S. Seelamantula, J. Kühn, M. Unser, and C. Depeursinge, “Suppression of the zero-order term in off-axis digital holography through non-linear filtering,” Appl. Opt. 48, H186–H195 (2009).

19. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).

20. R. C. Gonzalez and R. E. Woods, Thresholding: Digital Image Processing (Pearson, New York, 2018)