Lensless computational imaging through deep learning
Name
1702.08516.pdf
Size
14.31 MB
Format
Adobe PDF
Checksum (MD5)
558decee0f96f9230179d1579f54c241
Author(s)
Sinha, Ayan T
Lee, Justin
Li, Shuai
Barbastathis, George
Date Issued
September 2017
Journal
Optica
Publisher
Optical Society of America
Citation
Sinha, Ayan et al. “Lensless Computational Imaging through Deep Learning.” Optica 4, 9 (September 2017): 1117 © 2017 Optical Society of America
Version
Original manuscript
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.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1364/OPTICA.4.001117
