Quantitative phase microscopy using deep neural networks
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 gener...
Main Authors: | Li, Shuai (Contributor), Sinha, Ayan T (Contributor), Lee, Justin (Contributor), Barbastathis, George (Contributor) |
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Other Authors: | Institute for Medical Engineering and Science (Contributor), Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor) |
Format: | Article |
Language: | English |
Published: |
SPIE,
2018-11-16T15:37:22Z.
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Subjects: | |
Online Access: | Get fulltext |
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