Quantitative phase microscopy using deep neural networks

• Main Authors: Li, Shuai (Contributor), Sinha, Ayan T (Contributor), Lee, Justin (Contributor), Barbastathis, George (Contributor)
• 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.
• Subjects: Article
• Online Access: Get fulltext

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.
Singapore-MIT Alliance for Research and Technology (SMART)
United States. Department of Energy. Computational Science Graduate Fellowship Program (DE-FG02-97ER25308)
United States. Intelligence Advanced Research Projects Activity