Abstract
Digital holography is a well-known method to perform three-dimensional imaging by recording the light wavefront information originating from the object. Not only the intensity, but also the phase distribution of the wavefront can then be computed from the recorded hologram in the numerical reconstruction process. However, the reconstructions via the traditional methods suffer from various artifacts caused by twin-image, zero-order term, and noise from image sensors. Here we demonstrate that an end-to-end deep neural network (DNN) can learn to perform both intensity and phase recovery directly from an intensity-only hologram. We experimentally show that the artifacts can be effectively suppressed. Meanwhile, our network doesn't need any preprocessing for initialization, and is comparably fast to train and test, in comparison with the recently published learning-based method. In addition, we validate that the performance improvement can be achieved by introducing a prior on sparsity.
Original language | English |
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Title of host publication | High-Speed Biomedical Imaging and Spectroscopy III |
Subtitle of host publication | Toward Big Data Instrumentation and Management |
Publisher | SPIE |
Volume | 10505 |
ISBN (Electronic) | 9781510614956 |
DOIs | |
Publication status | Published - 1 Jan 2018 |
MoE publication type | A4 Article in a conference publication |
Event | High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management - San Francisco, United States Duration: 29 Jan 2018 → 30 Jan 2018 Conference number: 3 |
Conference
Conference | High-Speed Biomedical Imaging and Spectroscopy |
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Country/Territory | United States |
City | San Francisco |
Period | 29/01/2018 → 30/01/2018 |
Keywords
- Computational imaging
- Deep learning
- Deep neural network
- Digital holography
- Phase retrieval