Journal
QUANTITATIVE PHASE IMAGING IV
Volume 10503, Issue -, Pages -Publisher
SPIE-INT SOC OPTICAL ENGINEERING
DOI: 10.1117/12.2289056
Keywords
phase retrieval; deep neural network; wide-field microscopy
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Funding
- Singapore National Research Foun- dation through the SMART program (Singapore-MIT Alliance for Research and Technology)
- Intelligence Advanced Research Projects Activity (iARPA) through the RAVEN Program
- U.S. Department of Energy Computa- tional Science Graduate Fellowship (CSGF) [DE-FG02-97ER25308]
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Deep learning has been proven to achieve ground-breaking accuracy in various tasks. In this paper, we implemented a deep neural network (DNN) to achieve phase retrieval in a wide-field microscope. Our DNN utilized the residual neural network (ResNet) architecture and was trained using the data generated by a phase SLM. The results showed that our DNN was able to reconstruct the profile of the phase target qualitatively. In the meantime, large error still existed, which indicated that our approach still need to be improved.
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