Contribution to a conference proceedings/Contribution to a book

PUBDB-2023-00144

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Deep Iterative Phase Retrieval for Ptychography

Welker, S. (Corresponding author)CFEL* ; Peer, T.DESY* ; Chapman, H. N.CFEL*XFEL.EU*DESY* ; Gerkmann, T.

2022
IEEE Piscataway, NJ
ISBN: 978-1-6654-0540-9

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Please use a persistent id in citations: doi:10.1109/ICASSP43922.2022.9746811

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.

Note: Literaturangaben;

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Contributing Institute(s):

1. CFEL-Coherent X-Ray Imaging (FS-CFEL-1)

Research Program(s):

1. 633 - Life Sciences – Building Blocks of Life: Structure and Function (POF4-633) (POF4-633)
2. HIDSS-0002 - DASHH: Data Science in Hamburg - Helmholtz Graduate School for the Structure of Matter (2019_IVF-HIDSS-0002) (2019_IVF-HIDSS-0002)
3. Leibniz Preis - Leibiz Programm 2015: Prof. Dr. Henry N. Chapman (DFG-Leibniz-2015-Chapman) (DFG-Leibniz-2015-Chapman)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2022

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