ArticleJournal of synchrotron radiation2024
Using convolutional neural network denoising to reduce ambiguity in X-ray coherent diffraction imaging.
Article in Journal of synchrotron radiation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- From Static Structures to Molecular Dynamics: Emerging Directions in X-Ray and Electron Materials Characterization.Materials (Basel, Switzerland) · 2026Review
- Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education.Turkish journal of emergency medicineReview
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Authors and funding
8 authors.
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Abstract
The inherent ambiguity in reconstructed images from coherent diffraction imaging (CDI) poses an intrinsic challenge, as images derived from the same dataset under varying initial conditions often display inconsistencies. This study introduces a method that employs the Noise2Noise approach combined with neural networks to effectively mitigate these ambiguities. We applied this methodology to hundreds of ambiguous reconstructed images retrieved from a single diffraction pattern using a conventional retrieval algorithm. Our results demonstrate that ambiguous features in these reconstructions are effectively treated as inter-reconstruction noise and are significantly reduced. The post-Noise2Noise treated images closely approximate the average and singular value decomposition analysis of various reconstructions, providing consistent and reliable reconstructions.
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