@inproceedings{94eb4271151e43819b155c9a7eb8278a,
title = "Deep convolutional neural network-based lensless quantitative phase retrieval",
abstract = "In this paper, we employ a deep convolutional neural network for the solution of the phase retrieval problem in a lensless optical system from a single observation. We utilize U-net-like structured DCNN to reconstruct phase from the amplitude images at the sensor plane, and after applying computational backpropagation, the complex objects{\textquoteright} amplitude is reconstructed at the object plane. Results are demonstrated by simulation experiments.",
author = "Igor Shevkunov and Jarkko Kilpel{\"a}inen and Karen Eguiazarian",
note = "JUFOID=65546 Funding Information: Jane and Aatos Erkko Foundation and Finland Centennial Foundation funded Computational Imaging without Lens (CIWIL) project. Publisher Copyright: {\textcopyright} 2021 SPIE.; Quantitative Phase Imaging ; Conference date: 06-03-2021 Through 11-03-2021",
year = "2021",
doi = "10.1117/12.2581428",
language = "English",
isbn = "9781510641419",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Yang Liu and Gabriel Popescu and YongKeun Park",
booktitle = "Quantitative Phase Imaging VII",
address = "United States",
}