Literature DB >> 29791353

Deep-learning-generated holography.

Ryoichi Horisaki, Ryosuke Takagi, Jun Tanida.   

Abstract

We present a method for computer-generated holography based on deep learning. The inverse process of light propagation is regressed with a number of computationally generated speckle data sets. This method enables noniterative calculation of computer-generated holograms (CGHs). The proposed method was experimentally verified with a phase-only CGH.

Year:  2018        PMID： 29791353     DOI： 10.1364/AO.57.003859

Source DB:  PubMed          Journal:  Appl Opt        ISSN： 1559-128X            Impact factor:   1.980


  6 in total

1.  Towards real-time photorealistic 3D holography with deep neural networks.

Authors:  Liang Shi; Beichen Li; Changil Kim; Petr Kellnhofer; Wojciech Matusik
Journal:  Nature       Date:  2021-03-10       Impact factor: 49.962

Review 2.  Advances in computer-generated holography for targeted neuronal modulation.

Authors:  M Hossein Eybposh; Vincent R Curtis; Jose Rodríguez-Romaguera; Nicolas C Pégard
Journal:  Neurophotonics       Date:  2022-06-16       Impact factor: 4.212

3.  High-contrast, speckle-free, true 3D holography via binary CGH optimization.

Authors:  Byounghyo Lee; Dongyeon Kim; Seungjae Lee; Chun Chen; Byoungho Lee
Journal:  Sci Rep       Date:  2022-02-18       Impact factor: 4.379

4.  Comprehensive deep learning model for 3D color holography.

Authors:  Alim Yolalmaz; Emre Yüce
Journal:  Sci Rep       Date:  2022-02-15       Impact factor: 4.379

5.  End-to-end learning of 3D phase-only holograms for holographic display.

Authors:  Liang Shi; Beichen Li; Wojciech Matusik
Journal:  Light Sci Appl       Date:  2022-08-03       Impact factor: 20.257

Review 6.  Review of computer-generated hologram algorithms for color dynamic holographic three-dimensional display.

Authors:  Dapu Pi; Juan Liu; Yongtian Wang
Journal:  Light Sci Appl       Date:  2022-07-26       Impact factor: 20.257
  6 in total

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