Abstract
Computer-generated holograms (CGHs) are used in holographic three-dimensional (3D) displays and holographic projections. The quality of the reconstructed images using phase-only CGHs is degraded because the amplitude of the reconstructed image is difficult to control. Iterative optimization methods such as the Gerchberg–Saxton (GS) algorithm are one option for improving image quality. They optimize CGHs in an iterative fashion to obtain a higher image quality. However, such iterative computation is time-consuming, and the improvement in image quality is often stagnant. Recently, deep learning-based hologram computation has been proposed. Deep neural networks directly infer CGHs from input image data. However, it is limited to reconstructing images that are the same size as the hologram. In this study, we use deep learning to optimize phase-only CGHs generated using scaled diffraction computations and the random phase-free method. By combining the random phase-free method with the scaled diffraction computation, it is possible to handle a zoomable reconstructed image larger than the hologram. In comparison to the GS algorithm, the proposed method optimizes both high quality and speed.
Similar content being viewed by others
References
J.W. Goodman, Introduction to Fourier Optics (Roberts and Company Publishers, Greenwood Village, 2005)
T.-C. Poon, Digital Holography and Three-Dimensional Display: Principles and Applications (Springer, Berlin, 2006)
D. Binder, A. Ahar, S. Bettens, T. Birnbaum, A. Symeonidou, H. Ottevaere, C. Schretter, P. Schelkens, Signal Process. Image Commun. 70, 114–213 (2019)
K. Wakunami, P.-Y. Hsieh, R. Oi, T. Senoh, H. Sasaki, Y. Ichihashi, M. Okui, Y.-P. Huang, K. Yamamoto, Nat. Commun. 7, 12954 (2016)
K. Matsushima, N. Sonobe, Appl. Opt. 57, A150–A156 (2018)
L. Wei, Y. Sakamoto, Appl. Opt. 58, A258–A266 (2019)
Z. He, X. Sui, G. Jin, L. Cao, Appl. Opt. 58, A74–A81 (2019)
E. Buckley, J. Disp. Tech. 7, 135–140 (2011)
M. Makowski, I. Ducin, K. Kakarenko, J. Suszek, M. Sypek, A. Kolodziejczyk, Opt. Express 20, 25130–25136 (2012)
M. Makowski, Opt. Express 21, 29205–29216 (2013)
T. Shimobaba, M. Makowski, T. Kakue, M. Oikawa, N. Okada, Y. Endo, R. Hirayama, T. Ito, Opt. Express 21, 25285–25290 (2013)
M. Chlipała, T. Kozacki, H.-J. Yeom, J. Martinez-Carranza, R. Kukołowicz, J. Kim, J.-H. Yang, J.H. Choi, J.-E. Pi, C.-S. Hwang, Opt. Lett. 46, 4956–4959 (2021)
J. Amako, H. Miura, T. Sonehara, Appl. Opt. 34, 3165–3171 (1995)
P.W.M. Tsang, Y.-T. Chow, T.-C. Poon, Opt. Express 22, 25208–25214 (2014)
J.-P. Liu, M.-H. Wu, P.W.M. Tsang, Opt. Express 28, 24526–24537 (2020)
C.K. Hsueh, A.A. Sawchuk, Computer-generated double-phase holograms. Appl. Opt. 17, 3874–3883 (1978)
O. Mendoza-Yero, G. Mínguez-Vega, J. Lancis, Opt. Lett. 39, 1740–1743 (2014)
X. Sui, Z. He, G. Jin, D. Chu, L. Cao, Opt. Express 29, 2597–2612 (2021)
T. Shimobaba, T. Takahashi, Y. Yamamoto, I. Hoshi, A. Shiraki, T. Kakue, T. Ito, J. Opt. 22, 045703 (2020)
R.W. Gerchberg, Optik 35, 237–246 (1972)
M. Makowski, M. Sypek, A. Kolodziejczyk, G. Mikula, Opt. Eng. 44, 125805 (2005)
C. Chang, J. Xia, L. Yang, W. Lei, Z. Yang, J. Chen, Appl. Opt. 54, 6994–7001 (2015)
R. Horisaki, R. Takagi, J. Tanida, Appl. Opt. 57, 3859–3863 (2018)
J. Wu, K. Liu, X. Sui, L. Cao, Opt. Lett. 46, 2908–2911 (2021)
J.-W. Kang, B.-S. Park, J.-K. Kim, D.-W. Kim, Y.-H. Seo, Appl. Opt. 60, 7391–7399 (2021)
L. Shi, B. Li, C. Kim, P. Kellnhofer, W. Matusik, Nature 591, 234–239 (2021)
T. Shimobaba, T. Kakue, N. Okada, M. Oikawa, Y. Yamaguchi, T. Ito, J. Opt. 15, 075302 (2013)
T. Shimobaba, T. Ito, Opt. Express 23, 9549–9554 (2015)
T. Shimobaba, T. Kakue, Y. Endo, R. Hirayama, D. Hiyama, S. Hasegawa, Y. Nagahama, M. Sano, M. Oikawa, T. Sugie, T. Ito, Opt. Express 23, 17269–17274 (2015)
T. Shimobaba, T. Kakue, Y. Endo, R. Hirayama, D. Hiyama, S. Hasegawa, Y. Nagahama, M. Sano, M. Oikawa, T. Sugie, T. Ito, Opt. Commun. 355, 596–601 (2015)
Y. Nagahama, T. Shimobaba, T. Kakue, N. Masuda, T. Ito, Appl. Opt. 56, F61–F66 (2017)
Y. Nagahama, T. Shimobaba, T. Kakue, Y. Takaki, T. Ito, Appl. Opt. 58, 2146–2151 (2019)
O. Ronneberger, P. Fischer, T. Brox, Springer LNCS 9351, 234–241 (2015)
X. Li, J. Liu, J. Jia, Y. Pan, Y. Wang, Opt. Express 21, 20577–20587 (2013)
R.J. Collier, C.B. Burckhardt, L.H. Lin, Optical Holography (Academic Press, London, 1971)
I. Krasin, T. Duerig, N. Alldrin, V. Ferrari, S. Abu-El-Haija, A. Kuznetsova, H. Rom, J. Uijlings, S. Popov, S. Kamali, M. Malloci, J. Pont-Tuset, A. Veit, S. Belongie, V. Gomes, A. Gupta, C. Sun, G. Chechik, D. Cai, Z. Feng, D. Narayanan, K. Murphy. https://github.com/openimages.
A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset, S. Kamali, S. Popov, M. Malloci, A. Kolesnikov, T. Duerig, V. Ferrari, Int. J. Comput. Vis. 128, 1956–1981 (2020)
P.W.M. Tsang, T.-C. Poon, Opt. Express 21, 23680–23686 (2013)
J.-Y. Zhu, T. Park, P. Isola, A.A. Efros: In Proceedings of the IEEE international conference on computer vision, pp. 2223–2232 (2017).
Y. Wu, J. Wang, C. Chen, C.-J. Liu, F.G.-M. Jin, N. Chen, Adaptive weighted Gerchberg–Saxton algorithm for generation of phase-only hologram with artifacts suppression. Opt. Express 29, 1412–1427 (2021)
P.W.M. Tsang, Y.-T. Chow, T.-C. Poon, Generation of phase-only Fresnel hologram based on down-sampling. Opt. Express 22, 25208–25214 (2014)
Acknowledgements
This work was partially supported by Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Numbers 19H04132 and 19H1097, the joint JSPS-FWO scientific cooperation program (VS07820N) and the Research Foundation—Flanders (FWO), Junior postdoctoral fellowship (12ZQ220N).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ishii, Y., Shimobaba, T., Blinder, D. et al. Optimization of phase-only holograms calculated with scaled diffraction calculation through deep neural networks. Appl. Phys. B 128, 22 (2022). https://doi.org/10.1007/s00340-022-07753-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s00340-022-07753-7