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  4. Model-based autofocus for near-field phase retrieval
 
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Model-based autofocus for near-field phase retrieval

Citation Link: https://doi.org/10.15480/882.14883
Publikationstyp
Journal Article
Date Issued
2025-02-24
Sprache
English
Author(s)
Dora, Johannes 
Biomedizinische Bildgebung E-5  
Möddel, Martin  orcid-logo
Biomedizinische Bildgebung E-5  
Flenner, Silja  
Helmholtz-Zentrum Hereon  
Reimers, Jan  
Zeller-Plumhoff, Berit  
Schroer, Christian G.  
Knopp, Tobias  
Biomedizinische Bildgebung E-5  
Hagemann, Johannes  
TORE-DOI
10.15480/882.14883
TORE-URI
https://hdl.handle.net/11420/54729
Journal
Optics express  
Volume
33
Issue
4
Start Page
6641
End Page
6657
Citation
Optics Express 33 (4): 6641-6657 (2025)
Publisher DOI
10.1364/OE.544573
Scopus ID
2-s2.0-85218622641
Publisher
Optica Publishing Group
The phase problem is a well known ill-posed reconstruction problem of coherent lens-less microscopic imaging, where only the intensities of a complex wave-field are measured by the detector and the phase information is lost. For the reconstruction of sharp images from holograms in a near-field experimental setting, it is crucial to solve the autofocus problem, i.e., to precisely estimate the Fresnel number of the forward model. Otherwise, blurred out-of focus images that also can contain artifacts are the result. In general, a simple distance measurement at the experiment is not sufficiently accurate, thus the fine-tuning of the Fresnel number has to be done prior to the actual reconstructions. This can be done manually or automatically by an estimation algorithm. To automatize the process, as needed, e.g., for in-situ/operando experiments, different focus criteria have been widely studied in literature but are subjected to certain restrictions. The methods often rely on image analysis of the reconstructed image, making them sensitive to image noise and also neglecting algorithmic properties of the applied phase retrieval. In this paper, we propose a novel criterion, based on a model-matching approach, which improves autofocusing by also taking the underlying reconstruction algorithm, the forward model and the measured hologram into account. We derive a common autofocusing framework, based on a recent phase-retrieval approach and a downhill-simplex method for the automatic optimization of the Fresnel number. We further demonstrate the robustness of the framework on different data sets obtained at the nano imaging endstation of P05 at PETRA III (DESY, Hamburg) operated by Helmholtz-Zentrum Hereon.
DDC Class
530: Physics
Funding(s)
SFB 986: Subproject Z02 - Multi-scale analysis of structures and processes via synchrotron radiation and neutrons  
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by/4.0/
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