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@INPROCEEDINGS{Welker:491435,
author = {Welker, Simon and Peer, Tal and Chapman, Henry N. and
Gerkmann, Timo},
title = {{D}eep {I}terative {P}hase {R}etrieval for {P}tychography},
address = {Piscataway, NJ},
publisher = {IEEE},
reportid = {PUBDB-2023-00144},
isbn = {978-1-6654-0540-9},
pages = {1591-1595},
year = {2022},
note = {Literaturangaben;},
comment = {[Ebook] 2022 IEEE International Conference on Acoustics,
Speech, and Signal Processing : proceedings : 7-13 May 2022,
virtual (all paper presentations) : 22-27 May 2022, main
venue: Marina Bay Sands Expo $\&$ Convention Center,
Singapore, satellite venue: Shenzhen, China / sponsored by:
the Institute of Electrical and Electronics Engineers,
Signal Processing Society , Piscataway, NJ : IEEE, 2022,},
booktitle = {[Ebook] 2022 IEEE International
Conference on Acoustics, Speech, and
Signal Processing : proceedings : 7-13
May 2022, virtual (all paper
presentations) : 22-27 May 2022, main
venue: Marina Bay Sands Expo $\&$
Convention Center, Singapore, satellite
venue: Shenzhen, China / sponsored by:
the Institute of Electrical and
Electronics Engineers, Signal
Processing Society , Piscataway, NJ :
IEEE, 2022,},
abstract = {One of the most prominent challenges in the field of
diffractive imaging is the phase retrieval (PR) problem: In
order to reconstruct an object from its diffraction pattern,
the inverse Fourier transform must be computed. This is only
possible given the full complex-valued diffraction data,
i.e. magnitude and phase. However, in diffractive imaging,
generally only magnitudes can be directly measured while the
phase needs to be estimated. In this work we specifically
consider ptychography, a sub-field of diffractive imaging,
where objects arereconstructed from multiple overlapping
diffraction images. We pro- pose an augmentation of existing
iterative phase retrieval algorithms with a neural network
designed for refining the result of each iteration. For this
purpose we adapt and extend a recently proposed architecture
from the speech processing field. Evaluation results show
the proposed approach delivers improved convergence rates in
terms of both iteration count and algorithm runtime.},
month = {May},
date = {2022-05-23},
organization = {ICASSP 2022 - 2022 IEEE International
Conference on Acoustics, Speech and
Signal Processing , Singapore
(Singapore), 23 May 2022 - 27 May 2022},
cin = {FS-CFEL-1},
cid = {I:(DE-H253)FS-CFEL-1-20120731},
pnm = {633 - Life Sciences – Building Blocks of Life: Structure
and Function (POF4-633) / HIDSS-0002 - DASHH: Data Science
in Hamburg - Helmholtz Graduate School for the Structure of
Matter $(2019_IVF-HIDSS-0002)$ / Leibniz Preis - Leibiz
Programm 2015: Prof. Dr. Henry N. Chapman
(DFG-Leibniz-2015-Chapman)},
pid = {G:(DE-HGF)POF4-633 / $G:(DE-HGF)2019_IVF-HIDSS-0002$ /
G:(DE-H253)DFG-Leibniz-2015-Chapman},
experiment = {EXP:(DE-MLZ)NOSPEC-20140101},
typ = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
doi = {10.1109/ICASSP43922.2022.9746811},
url = {https://bib-pubdb1.desy.de/record/491435},
}