Journal
OPTICA
Volume 7, Issue 6, Pages 559-562Publisher
OPTICAL SOC AMER
DOI: 10.1364/OPTICA.389314
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Funding
- Swiss National Science Foundation [P2ELP2 17227]
- National Science Foundation [DMR 154892, DGE 1106400, ONRN00014-17-1-2401]
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Deep neural networks have emerged as effective tools for computational imaging, including quantitative phase microscopy of transparent samples. To reconstruct phase from intensity, current approaches rely on supervised learning with training examples; consequently, their performance is sensitive to a match of training and imaging settings. Here we propose a new approach to phase microscopy by using an untrained deep neural network for measurement formation, encapsulating the image prior and the system physics. Our approach does not require any training data and simultaneously reconstructs the phase and pupil-plane aberrations by fitting the weights of the network to the captured images. To demonstrate experimentally, we reconstruct quantitative phase from through-focus intensity images without knowledge of the aberrations. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.
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