Lensless computational imaging through deep learning
Deep learning has been proven to yield reliably generalizable solutions to numerous classification and decision tasks. Here, we demonstrate for the first time to our knowledge that deep neural networks (DNNs) can be trained to solve end-to-end inverse problems in computational imaging. We experiment...
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| Published in | Optica Vol. 4; no. 9; p. 1117 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Optical Society of America
20.09.2017
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Deep learning has been proven to yield reliably generalizable solutions to numerous classification and decision tasks. Here, we demonstrate for the first time to our knowledge that deep neural networks (DNNs) can be trained to solve end-to-end inverse problems in computational imaging. We experimentally built and tested a lensless imaging system where a DNN was trained to recover phase objects given their propagated intensity diffraction patterns. |
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| AbstractList | Deep learning has been proven to yield reliably generalizable solutions to numerous classification and decision tasks. Here, we demonstrate for the first time to our knowledge that deep neural networks (DNNs) can be trained to solve end-to-end inverse problems in computational imaging. We experimentally built and tested a lensless imaging system where a DNN was trained to recover phase objects given their propagated intensity diffraction patterns. |
| Author | Sinha, Ayan Barbastathis, George Li, Shuai Lee, Justin |
| Author_xml | – sequence: 1 givenname: Ayan surname: Sinha fullname: Sinha, Ayan – sequence: 2 givenname: Justin surname: Lee fullname: Lee, Justin – sequence: 3 givenname: Shuai surname: Li fullname: Li, Shuai – sequence: 4 givenname: George surname: Barbastathis fullname: Barbastathis, George |
| BackLink | https://www.osti.gov/biblio/1390388$$D View this record in Osti.gov |
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