Using convolutional neural network denoising to reduce ambiguity in X-ray coherent diffraction imaging

Autor:	Kang-Ching Chu, Chia-Hui Yeh, Jhih-Min Lin, Chun-Yu Chen, Chi-Yuan Cheng, Yi-Qi Yeh, Yu-Shan Huang, Yi-Wei Tsai
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	coherent diffraction imaging machine learning noise2noise mixed-scale dense network Nuclear and particle physics. Atomic energy. Radioactivity QC770-798 Crystallography QD901-999
Zdroj:	Journal of Synchrotron Radiation, Vol 31, Iss 5, Pp 1340-1345 (2024)
Druh dokumentu:	article
ISSN:	1600-5775 16005775
DOI:	10.1107/S1600577524006519
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/1e4afb72d8e24590bdbdeb816c6dd9cf Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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