Improving image reconstruction for ultra-fast ptychographic acquisitions via deep learning denoising

Erin, E., Fardin, L., Batey, D., Burian, M., Vogel, S., Grimm, S., Fratini, M., Palombo, M. ORCID: https://orcid.org/0000-0003-4892-7967, Zhou, Fenglei, Parker, Geoff J. M., Olivo, A. and Cipiccia, S. 2025. Improving image reconstruction for ultra-fast ptychographic acquisitions via deep learning denoising. Presented at: 15th International Conference on Synchrotron Radiation Instrumentation (SRI 2024), Hamburg, Germany, 26-30 August 2024. Journal of Physics: Conference Series. , vol.3010 (1) IOP Publishing, 10.1088/1742-6596/3010/1/012172

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Abstract

X-ray ptychography is a scanning coherent diffraction imaging technique which combines nanometer-scale resolution with high penetration depth. This method has been proven to be suitable for scanning weakly absorbing samples and therefore potentially very valuable for medical applications such as brain imaging. However, currently employed scanning techniques present challenges: step-scanning is too slow and inefficient, while fly-scanning introduces blurring and noise into reconstructions due to the motion and reduced photon counts per pixel. To date, only a few methods have been proposed to denoise reconstructions, most of which rely on traditional approaches and are limited in addressing the challenges posed by noise and blurring. To overcome these limitations, we investigate the possibility of using a deep learning-based denoising method combined with position binning. The deep learning-based denoising method, Deep Image Prior (DIP), denoises the reconstructions while position binning increases the photon count statistics per pixel. The method can be integrated within the existing iterative phase retrieval algorithms to denoise the object or probe in between iterations. The method is tested in far-field geometry on two different samples: a Siemens star resolution target and a polymer-based phantom mimicking the white matter of the brain. By assessing the resolution via Fourier ring correlation, we measure up to a 14% increase in the resolution. However, depending on the architecture used, artifacts due to machine hallucination appear in the denoised images which could be affecting the observed enhancement in resolution. This will be the subject of further investigation.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Schools > Psychology Research Institutes & Centres > Cardiff University Brain Research Imaging Centre (CUBRIC)
Additional Information:	License information from Publisher: LICENSE 1: URL: https://creativecommons.org/licenses/by/4.0/, Type: cc-by
Publisher:	IOP Publishing
ISSN:	1742-6588
Date of First Compliant Deposit:	16 June 2025
Last Modified:	16 Jun 2025 12:45
URI:	https://orca.cardiff.ac.uk/id/eprint/179092

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