Learning Based Holographic Reconstruction through a Diffuser

Lina Zhou, Yin Xiao, Wen Chen

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


Object recovery from speckle patterns has been extensively studied, and holographic reconstruction technique is verified to be highly effective for recovering objects. However, for the holograms recorded through scattering media, conventional holographic techniques cannot retrieve useful information from the holograms. In this paper, we present an approach based on convolution neural network (CNN) for holographic reconstruction through a diffuser. The object is placed behind a diffuser, and the corresponding hologram is recorded by a CCD camera. With pairs of holograms and their original objects sent to the designed learning structure, a CNN model is trained to perform unknown-object retrieval from the holograms. This learning-based approach can make predictions of the unknown test objects in real time. It provides a feasible structure to conduct object recovery from the holograms recorded through a diffuser.

Original languageEnglish
Title of host publication2019 PhotonIcs and Electromagnetics Research Symposium - Spring, PIERS-Spring 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781728134031
Publication statusPublished - 17 Jun 2019
Event2019 PhotonIcs and Electromagnetics Research Symposium - Spring, PIERS-Spring 2019 - Rome, Italy
Duration: 17 Jun 201920 Jun 2019

Publication series

NameProgress in Electromagnetics Research Symposium
ISSN (Print)1559-9450
ISSN (Electronic)1931-7360


Conference2019 PhotonIcs and Electromagnetics Research Symposium - Spring, PIERS-Spring 2019

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

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