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

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.

SUBMITTER: Chu KC

PROVIDER: S-EPMC11371064 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature

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

Chu Kang Ching KC Yeh Chia Hui CH Lin Jhih Min JM Chen Chun Yu CY Cheng Chi Yuan CY Yeh Yi Qi YQ Huang Yu Shan YS Tsai Yi Wei YW

Journal of synchrotron radiation 20240805 Pt 5

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 conventiona ...[more]

PMID: 39102364

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