Autofocusing using deep learning in off-axis digital holography

Jaesung Lee, Wooyoung Jeong, Kyungchan Son, Wonseok Jeon, Hyunseok Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We have focused on rapid and efficient estimator to find object distance from hologram in order to reconstruct original image. Our approach to find it makes the estimator pre-trained through deep learning. Especially in off-axis holography configuration, our method eliminates the unnecessary factors and reduces information loss occurred by resizing image to plug into Convolution Neural Network (CNN). Training is performed on the generated images at several specific distances under various optical conditions and the accuracy of estimation is validated.

Original languageEnglish
Title of host publicationDigital Holography and Three-Dimensional Imaging, DH 2018
PublisherOSA - The Optical Society
ISBN (Electronic)9781557528209
DOIs
Publication statusPublished - 2018 Jan 1
EventDigital Holography and Three-Dimensional Imaging, DH 2018 - Orlando, United States
Duration: 2018 Jun 252018 Jun 28

Publication series

NameOptics InfoBase Conference Papers
VolumePart F100-DH 2018

Other

OtherDigital Holography and Three-Dimensional Imaging, DH 2018
CountryUnited States
CityOrlando
Period18/6/2518/6/28

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Mechanics of Materials

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  • Cite this

    Lee, J., Jeong, W., Son, K., Jeon, W., & Yang, H. (2018). Autofocusing using deep learning in off-axis digital holography. In Digital Holography and Three-Dimensional Imaging, DH 2018 (Optics InfoBase Conference Papers; Vol. Part F100-DH 2018). OSA - The Optical Society. https://doi.org/10.1364/DH.2018.DTh1C.4