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Convergence properties of ePIE and stochastic gradient methods for blind ptychography.
Anal. Appl., DOI: 10.1142/S0219530525500216 (2025)
We consider blind ptychography, an imaging technique which aims to reconstruct an object of interest from a set of its diffraction patterns each obtained by a local illumination. As the distribution of the light within the illuminated region, called the window, is not known, it has to be estimated as well. For the recovery, a minimization of amplitude-base squared loss via gradient and stochastic gradient descent methods is considered. In particular, this includes extended Ptychographic Iterative Engine as a special case of stochastic gradient descent. We show that with a proper choice of step sizes, all methods converge to a critical point at a sublinear rate and discuss possibilities for larger step sizes.
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1.440
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Blind Ptychography ; Extended Ptychographic Iterative Engine ; Gradient Descent ; Stochastic Gradient Descent; Phase Retrieval Uniqueness; Diffraction; Microscopy; Algorithm; Recovery; Image
Language
english
Publication Year
2025
HGF-reported in Year
2025
ISSN (print) / ISBN
0219-5305
e-ISSN
1793-6861
Journal
Analysis and Applications
Publisher
World Scientific Publishing
Publishing Place
5 Toh Tuck Link, Singapore 596224, Singapore
Reviewing status
Peer reviewed
Institute(s)
Institute of Biological and Medical Imaging (IBMI)
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-505595-001
Grants
Helmholtz Association
WOS ID
001472712700001
Scopus ID
105003477245
Erfassungsdatum
2025-05-09