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|a 10.1107/S1600577524006519
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|a eng
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| 100 |
1 |
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|a Chu, Kang Ching
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Using convolutional neural network denoising to reduce ambiguity in X-ray coherent diffraction imaging
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|c 2024
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 05.09.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a open access.
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|a 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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|a Journal Article
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|a Noise2Noise
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|a coherent diffraction imaging
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| 650 |
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|a machine learning
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| 650 |
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4 |
|a mixed-scale dense network
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| 700 |
1 |
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|a Yeh, Chia Hui
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lin, Jhih Min
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Chun Yu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cheng, Chi Yuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yeh, Yi Qi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Huang, Yu Shan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tsai, Yi Wei
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Journal of synchrotron radiation
|d 1994
|g 31(2024), Pt 5 vom: 01. Sept., Seite 1340-1345
|w (DE-627)NLM09824129X
|x 1600-5775
|7 nnas
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| 773 |
1 |
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|g volume:31
|g year:2024
|g number:Pt 5
|g day:01
|g month:09
|g pages:1340-1345
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| 856 |
4 |
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|u http://dx.doi.org/10.1107/S1600577524006519
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