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Using convolutional neural network denoising to reduce ambiguity in X-ray coherent diffraction imaging.
Chu, Kang Ching; Yeh, Chia Hui; Lin, Jhih Min; Chen, Chun Yu; Cheng, Chi Yuan; Yeh, Yi Qi; Huang, Yu Shan; Tsai, Yi Wei.
Afiliação
  • Chu KC; National Synchrotron Radiation Research Center, Hsinchu 300, Taiwan.
  • Yeh CH; Department of Physics, National Tsing Hua University, Hsinchu 300, Taiwan.
  • Lin JM; National Synchrotron Radiation Research Center, Hsinchu 300, Taiwan.
  • Chen CY; National Synchrotron Radiation Research Center, Hsinchu 300, Taiwan.
  • Cheng CY; Department of Physics, National Tsing Hua University, Hsinchu 300, Taiwan.
  • Yeh YQ; National Synchrotron Radiation Research Center, Hsinchu 300, Taiwan.
  • Huang YS; National Synchrotron Radiation Research Center, Hsinchu 300, Taiwan.
  • Tsai YW; National Synchrotron Radiation Research Center, Hsinchu 300, Taiwan.
J Synchrotron Radiat ; 31(Pt 5): 1340-1345, 2024 Sep 01.
Article em En | MEDLINE | ID: mdl-39102364
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: J Synchrotron Radiat Assunto da revista: RADIOLOGIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Taiwan País de publicação: Estados Unidos
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: J Synchrotron Radiat Assunto da revista: RADIOLOGIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Taiwan País de publicação: Estados Unidos