Literature DB >> 33690416

Three-dimensional deeply generated holography [Invited].

Ryoichi Horisaki, Yohei Nishizaki, Katsuhisa Kitaguchi, Mamoru Saito, Jun Tanida.   

Abstract

In this paper, we present a noniterative method for 3D computer-generated holography based on deep learning. A convolutional neural network is adapted for directly generating a hologram to reproduce a 3D intensity pattern in a given class. We experimentally demonstrated the proposed method with optical reproductions of multiple layers based on phase-only Fourier holography. Our method is noniterative, but it achieves a reproduction quality comparable with that of iterative methods for a given class.

Year:  2021        PMID: 33690416     DOI: 10.1364/AO.404151

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


  1 in total

1.  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

  1 in total

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