Single-Image Super-Resolution Improvement of X-ray Single-Particle Diffraction Images Using a Convolutional Neural Network

Tokuhisa, Atsushi; Akinaga, Yoshinobu; Terayama, Kei; Okamoto, Yuji; Okuno, Yasushi

doi:10.1021/acs.jcim.2c00660

Cited by 2 publications

(1 citation statement)

References 49 publications

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“…And finally, DL can be used to improve the resolution of TEM images beyond the physical limits of the microscope, by predicting high-resolution features from lower-resolution images. Super-resolution examples [100][101][102][103].…”

Section: Introductionmentioning

confidence: 99%

Advancing electron microscopy using deep learning

Chen,

Barnard

2024

J. Phys. Mater.

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Electron microscopy, a sub-field of microanalysis, is a victim of its own success. The widespread use of electron microscopy for imaging molecules and materials has had an enormous impact on our understanding of countless systems and has accelerated impacts in drug discovery and materials design, for electronic, energy, environment and health applications. With this success a bottleneck has emerged, as the rate at which we can collect data has significantly exceeded the rate at which we can analyse it. Fortunately, this has coincided with the rise of advanced computational methods, including data science and machine learning. Deep learning, a sub-field of machine learning capable of learning from large quantities of data such as images, is ideally suited to overcome some of the challenges of electron microscopy at scale. There are a variety of different deep learning approaches relevant to the field, with unique advantages and disadvantages. In this review, we describe some well-established methods, with some recent examples, and introduce some new methods currently emerging in computer science. Our summary of deep learning is designed to guide electron microscopists to choose the right deep learning algorithm for their research and prepare for their digital future.

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Section: Introductionmentioning

confidence: 99%

Advancing electron microscopy using deep learning

Chen,

Barnard

2024

J. Phys. Mater.

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Sub-photon accuracy noise reduction of a single shot coherent diffraction pattern with an atomic model trained autoencoder

Ishikawa,

Takeo,

Sakurai

et al. 2024

Opt. Express

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Single-shot imaging with femtosecond X-ray lasers is a powerful measurement technique that can achieve both high spatial and temporal resolution. However, its accuracy has been severely limited by the difficulty of applying conventional noise-reduction processing. This study uses deep learning to validate noise reduction techniques, with autoencoders serving as the learning model. Focusing on the diffraction patterns of nanoparticles, we simulated a large dataset treating the nanoparticles as composed of many independent atoms. Three neural network architectures are investigated: neural network, convolutional neural network and U-net, with U-net showing superior performance in noise reduction and subphoton reproduction. We also extended our models to apply to diffraction patterns of particle shapes different from those in the simulated data. We then applied the U-net model to a coherent diffractive imaging study, wherein a nanoparticle in a microfluidic device is exposed to a single X-ray free-electron laser pulse. After noise reduction, the reconstructed nanoparticle image improved significantly even though the nanoparticle shape was different from the training data, highlighting the importance of transfer learning.

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Single-Image Super-Resolution Improvement of X-ray Single-Particle Diffraction Images Using a Convolutional Neural Network

Cited by 2 publications

References 49 publications

Advancing electron microscopy using deep learning

Advancing electron microscopy using deep learning

Sub-photon accuracy noise reduction of a single shot coherent diffraction pattern with an atomic model trained autoencoder

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