Literature DB >> 31568437

Y-Net: a one-to-two deep learning framework for digital holographic reconstruction.

Kaiqiang Wang, Jiazhen Dou, Qian Kemao, Jianglei Di, Jianlin Zhao.   

Abstract

In this Letter, for the first time, to the best of our knowledge, we propose a digital holographic reconstruction method with a one-to-two deep learning framework (Y-Net). Perfectly fitting the holographic reconstruction process, the Y-Net can simultaneously reconstruct intensity and phase information from a single digital hologram. As a result, this compact network with reduced parameters brings higher performance than typical network variants. The experimental results of the mouse phagocytes demonstrate the advantages of the proposed Y-Net.

Entities:  

Year:  2019        PMID: 31568437     DOI: 10.1364/OL.44.004765

Source DB:  PubMed          Journal:  Opt Lett        ISSN: 0146-9592            Impact factor:   3.776


  5 in total

1.  HoloPhaseNet: fully automated deep-learning-based hologram reconstruction using a conditional generative adversarial model.

Authors:  Keyvan Jaferzadeh; Thomas Fevens
Journal:  Biomed Opt Express       Date:  2022-06-27       Impact factor: 3.562

2.  Video-Rate Quantitative Phase Imaging Using a Digital Holographic Microscope and a Generative Adversarial Network.

Authors:  Raul Castaneda; Carlos Trujillo; Ana Doblas
Journal:  Sensors (Basel)       Date:  2021-12-01       Impact factor: 3.576

3.  Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization.

Authors:  Hanlong Chen; Luzhe Huang; Tairan Liu; Aydogan Ozcan
Journal:  Light Sci Appl       Date:  2022-08-16       Impact factor: 20.257

4.  A Two-To-One Deep Learning General Framework for Image Fusion.

Authors:  Pan Zhu; Wanqi Ouyang; Yongxing Guo; Xinglin Zhou
Journal:  Front Bioeng Biotechnol       Date:  2022-07-14

5.  Automatic detection of synaptic partners in a whole-brain Drosophila electron microscopy data set.

Authors:  Julia Buhmann; Arlo Sheridan; Caroline Malin-Mayor; Philipp Schlegel; Stephan Gerhard; Tom Kazimiers; Renate Krause; Tri M Nguyen; Larissa Heinrich; Wei-Chung Allen Lee; Rachel Wilson; Stephan Saalfeld; Gregory S X E Jefferis; Davi D Bock; Srinivas C Turaga; Matthew Cook; Jan Funke
Journal:  Nat Methods       Date:  2021-06-24       Impact factor: 28.547

  5 in total

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