Phase recovery and holographic image reconstruction using deep learning in neural networks.

Journal: Light, science & applications
Published Date: Feb 23, 2018

Abstract

Phase recovery from intensity-only measurements forms the heart of coherent imaging techniques and holography. In this study, we demonstrate that a neural network can learn to perform phase recovery and holographic image reconstruction after appropriate training. This deep learning-based approach provides an entirely new framework to conduct holographic imaging by rapidly eliminating twin-image and self-interference-related spatial artifacts. This neural network-based method is fast to compute and reconstructs phase and amplitude images of the objects using only one hologram, requiring fewer measurements in addition to being computationally faster. We validated this method by reconstructing the phase and amplitude images of various samples, including blood and Pap smears and tissue sections. These results highlight that challenging problems in imaging science can be overcome through machine learning, providing new avenues to design powerful computational imaging systems.

Authors

  • Yair Rivenson
    Electrical and Computer Engineering Department, Bioengineering Department, University of California, Los Angeles, CA 90095 USA, and also with the California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA.
  • Yibo Zhang
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA.
  • Harun Günaydın
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA.
  • Da Teng
    Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
  • Aydogan Ozcan
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals