@inproceedings{75b6a9a919f4430f80e6ee6e18a37573,
title = "Deep learning approaches for unwrapping phase images with steep spatial gradients: A simulation",
abstract = "We explore different deep learning-based approaches to solve the problem of phase unwrapping in objects with high spatial gradients, which is applicable to many fields in medicine, biology and remote sensing. We simulate data with high spatial gradients to compare the quality of the solution and the runtime obtained when addressing this problem either as an inverse problem or as a semantic segmentation problem.",
keywords = "Deep learning, inverse problems, phase imaging, phase unwrapping, semantic segmentation",
author = "Gili Dardikman and Turko, {Nir A.} and Shaked, {Natan T.}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018 ; Conference date: 12-12-2018 Through 14-12-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/ICSEE.2018.8646266",
language = "English",
series = "2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018",
publisher = "Institute of Electrical and Electronics Engineers",
booktitle = "2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018",
address = "United States",
}