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Phase and absorbance retrieval in X‐ray holographic microscopy under weak illumination using physics‐driven neural networks.

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Title: Phase and absorbance retrieval in X‐ray holographic microscopy under weak illumination using physics‐driven neural networks.
Authors: Kim, Jihwan1 (AUTHOR), Lim, Jun2 (AUTHOR), Jo, Sugeun2 (AUTHOR), Park, Sungho3 (AUTHOR), Lee, Sang Joon1 (AUTHOR) sjlee@postech.ac.kr
Source: Journal of Synchrotron Radiation. May2026, Vol. 33 Issue 3, p794-805. 12p.
Subjects: X-ray microscopy, Deep learning, Densitometry, Three-dimensional imaging, Image reconstruction
Abstract: X‐ray holographic microscopy is a three‐dimensional (3D) imaging technique for nanoscale‐resolution imaging of morphological features and phase contrast in biological samples and solid‐state materials. However, it is challenging to recover phase and absorbance information from shot‐noise‐limited (SNL) X‐ray holograms acquired under weak illumination. In this study, we propose a deep learning model, named MorpHoloNet‐X, for single‐shot phase and absorbance retrieval from SNL X‐ray holograms using a physics‐driven neural network. By incorporating physics‐based prior knowledge and wave propagation principles into the neural network, MorpHoloNet‐X can directly reconstruct 3D complex wavefield, phase, and absorbance distributions in a simulated 3D volume. The performance of the proposed MorpHoloNet‐X is validated using synthetic and experimental SNL holograms, and the results are compared with those of conventional methods. The proposed technique would be utilized to reconstruct phase and absorbance information from hard X‐ray holograms acquired under rapid acquisition or weak illumination. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Synchrotron Radiation is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Phase and absorbance retrieval in X‐ray holographic microscopy under weak illumination using physics‐driven neural networks.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Synchrotron+Radiation%22">Journal of Synchrotron Radiation</searchLink>. May2026, Vol. 33 Issue 3, p794-805. 12p.
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  Data: <searchLink fieldCode="DE" term="%22X-ray+microscopy%22">X-ray microscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Densitometry%22">Densitometry</searchLink><br /><searchLink fieldCode="DE" term="%22Three-dimensional+imaging%22">Three-dimensional imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Image+reconstruction%22">Image reconstruction</searchLink>
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  Label: Abstract
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  Data: X‐ray holographic microscopy is a three‐dimensional (3D) imaging technique for nanoscale‐resolution imaging of morphological features and phase contrast in biological samples and solid‐state materials. However, it is challenging to recover phase and absorbance information from shot‐noise‐limited (SNL) X‐ray holograms acquired under weak illumination. In this study, we propose a deep learning model, named MorpHoloNet‐X, for single‐shot phase and absorbance retrieval from SNL X‐ray holograms using a physics‐driven neural network. By incorporating physics‐based prior knowledge and wave propagation principles into the neural network, MorpHoloNet‐X can directly reconstruct 3D complex wavefield, phase, and absorbance distributions in a simulated 3D volume. The performance of the proposed MorpHoloNet‐X is validated using synthetic and experimental SNL holograms, and the results are compared with those of conventional methods. The proposed technique would be utilized to reconstruct phase and absorbance information from hard X‐ray holograms acquired under rapid acquisition or weak illumination. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Synchrotron Radiation is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1107/S1600577526003188
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      – Code: eng
        Text: English
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        PageCount: 12
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        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Densitometry
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      – SubjectFull: Three-dimensional imaging
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      – SubjectFull: Image reconstruction
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      – TitleFull: Phase and absorbance retrieval in X‐ray holographic microscopy under weak illumination using physics‐driven neural networks.
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            NameFull: Jo, Sugeun
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            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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