Abstract
This study proposes a dynamic-range compression for digital holograms generated from three-dimensional scenes using deep neural network (DNN). This method uses an error diffusion algorithm to binarize holograms with an 8-bit gradation; moreover, the DNN predicts the original gradation holograms from binary holograms. This method’s performance exceeds that of JPEG 2000 and high-efficiency video coding.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
D. Blinder, A. Ahar, S. Bettens, T. Birnbaum, A. Symeonidou, H. Ottevaere, P. Schelkens, Signal processing challenges for digital holographic video display systems. Sig. Process. Image 70, 114–130 (2019)
M.V. Bernardo, P. Fernandes, A. Arrifano, M. Antonini, E. Fonseca, P.T. Fiadeiro, M. Pereira, Holographic representation: hologram plane versus object plane. Sig. Process. Image 68, 193–206 (2018)
P.A. Kochańska, M. Makowski, Compression of computer-generated holograms in image projection. Photon. Lett. Pol. 9, 60–62 (2017)
P. Tsang, W.K. Cheung, T. Kim, Y.S. Kim, T.C. Poon, Low-complexity compression of holograms based on delta modulation. Opt. Commun. 284, 2113–2117 (2011)
P. Tsang, K.W. Cheung, T.C. Poon, Low-bit-rate computer-generated color Fresnel holography with compression ratio of over 1600 times using vector quantization. Appl. Opt. 50, H42–H49 (2011)
Y. Rivenson, Y. Zhang, H. Günaydın, D. Teng, A. Ozcan, Phase recovery and holographic image reconstruction using deep learning in neural networks. Light Sci. Appl. 7, 17141 (2018)
R. Horisaki, R. Takagi, J. Tanida, Deep-learning-generated holography. Appl. Opt. 57, 3859–3863 (2018)
S. Jiao, Z. Jin, C. Chang, C. Zhou, W. Zou, X. Li, Compression of phase-only holograms with JPEG standard and deep learning. Appl. Sci. 8, 1258 (2018)
T. Shimobaba, D. Blinder, M. Makowski, P. Schelkens, Y. Yamamoto, I. Hoshi, T. Nishitsuji, Y. Endo, T. Kakue, T. Ito, Dynamic-range compression scheme for digital hologram using a deep neural network. Opt. Lett. 44, 3038–3041 (2019)
N. Okada, T. Shimobaba, Y. Ichihashi, R. Oi, K. Yamamoto, M. Oikawa, T. Kakue, N. Masuda, T. Ito, Band-limited double-step Fresnel diffraction and its application to computer-generated holograms. Opt. Exp. 21, 9192–9197 (2013)
O. Ronneberger, P. Fischer, T. Brox, U-net: convolutional networks for biomedical image segmentation, in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), pp. 234–241
T. Shimobaba, J. Weng, T. Sakurai, N. Okada, T. Nishitsuji, N. Takada, T. Ito, Computational wave optics library for C++: CWO++ library. Comput. Phys. Commun. 183, 1124–1138 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shimobaba, T. et al. (2021). Deep-Learning-Based Dynamic Range Compression for 3D Scene Hologram. In: Singh, K., Gupta, A.K., Khare, S., Dixit, N., Pant, K. (eds) ICOL-2019. Springer Proceedings in Physics, vol 258. Springer, Singapore. https://doi.org/10.1007/978-981-15-9259-1_10
Download citation
DOI: https://doi.org/10.1007/978-981-15-9259-1_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-9258-4
Online ISBN: 978-981-15-9259-1
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)