Deep-learning-based binary hologram
- 10 August 2020
- journal article
- research article
- Published by Optica Publishing Group in Applied Optics
- Vol. 59 (23), 7103-7108
- https://doi.org/10.1364/ao.393500
Abstract
Binary hologram generation based on deep learning is proposed. The proposed method can reduce the severe effect of quality degradation from binarizing gray-scaled holograms by optimizing the neural network to output binary amplitude holograms directly. In previous work on binary holograms, the calculation time for generating binary holograms was long. However, in the proposed method, once the neural network is trained enough, the neural network generates binary holograms much faster than previous work with comparable quality. The proposed method is more suitable for opportunities to generate several binary holograms under the same condition. The feasibility of the proposed method was confirmed experimentally. (C) 2020 Optical Society of America.This publication has 18 references indexed in Scilit:
- Deep learning microscopyOptica, 2017
- Single-shot phase imaging with randomized light (SPIRaL)Optics Express, 2016
- Binary hologram generation based on discrete wavelet transformOptik, 2016
- U-Net: Convolutional Networks for Biomedical Image SegmentationPublished by Springer Science and Business Media LLC ,2015
- Three-dimensional display technologiesAdvances in Optics and Photonics, 2013
- Computer generation of binary Fresnel holography.Applied Optics, 2011
- Gradual and random binarization of gray-scale hologramsApplied Optics, 1995
- Fresnel ping-pong algorithm for two-plane computer-generated hologram displayApplied Optics, 1994
- Computer-generated Binary HologramsIBM Journal of Research and Development, 1969
- Binary Fraunhofer Holograms, Generated by ComputerApplied Optics, 1967