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Sub-photon accuracy noise reduction of single shot coherent diffraction pattern with atomic model trained autoencoder (2403.11992v1)

Published 18 Mar 2024 in physics.optics and eess.IV

Abstract: 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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References (37)
  1. T. Ishikawa, H. Aoyagi, T. Asaka, et al., “A compact x-ray free-electron laser emitting in the sub-ångström region,” \JournalTitleNature Photonics 6, 540–544 (2012).
  2. P. Emma, R. Akre, J. Arthur, et al., “First lasing and operation of an ångstrom-wavelength free-electron laser,” \JournalTitleNature Photonics 4, 641–647 (2010).
  3. R. Neutze, R. Wouts, D. van der Spoel, et al., “Potential for biomolecular imaging with femtosecond x-ray pulses,” \JournalTitleNature 406, 752–757 (2000).
  4. H. N. Chapman, A. Barty, M. J. Bogan, et al., “Femtosecond diffractive imaging with a soft-x-ray free-electron laser,” \JournalTitleNature Physics 2, 839–843 (2006).
  5. M. M. Seibert, T. Ekeberg, F. R. N. C. Maia, et al., “Single mimivirus particles intercepted and imaged with an x-ray laser,” \JournalTitleNature 470, 78–81 (2011).
  6. T. Kimura, Y. Joti, A. Shibuya, et al., “Imaging live cell in micro-liquid enclosure by x-ray laser diffraction,” \JournalTitleNature Communications 5, 3052 (2014).
  7. G. Van Der Schot, M. Svenda, F. Maia, et al., “Imaging single cells in a beam of live cyanobacteria with an x-ray laser,” \JournalTitleNature communications 6 (2015). Publisher Copyright: © 2014 Macmillan Publishers Limited. All rights reserved.
  8. Y. Matsumoto, Y. Takeo, S. Egawa, et al., “An arrayed-window microfluidic device for observation of mixed nanoparticles with an x-ray free-electron laser,” \JournalTitleOptical Review 29, 7–12 (2022).
  9. A. Suzuki, Y. Niida, Y. Joti, et al., “Damage-Free femtosecond X-Ray laser snapshot imaging of catalyst layer Nano-Structures of polymer electrolyte fuel cells,” \JournalTitleECS Meeting Abstracts MA2022-02, 1419 (2022).
  10. D. T. Kuan, A. A. Sawchuk, T. C. Strand, and P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” \JournalTitleIEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-7, 165–177 (1985).
  11. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” \JournalTitleIEEE Transactions on Image Processing 16, 2080–2095 (2007).
  12. A. Buades, B. Coll, and J. M. Morel, “A review of image denoising algorithms, with a new one,” \JournalTitleMultiscale Modeling & Simulation 4, 490–530 (2005).
  13. D. Donoho, “Compressed sensing,” \JournalTitleIEEE Transactions on Information Theory 52, 1289–1306 (2006).
  14. M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” \JournalTitleIEEE Transactions on Image Processing 15, 3736–3745 (2006).
  15. J. Otsuki, M. Ohzeki, H. Shinaoka, and K. Yoshimi, “Sparse modeling in quantum many-body problems,” \JournalTitleJournal of the Physical Society of Japan 89, 012001 (2020).
  16. P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in International Conference on Machine Learning proceedings, (In International Conference on Machine Learning. 1096–1103, 2008).
  17. P. Vincent, H. Larochelle, I. Lajoie, et al., “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.” \JournalTitleJ. Mach. Learn. Res. 11, 3371–3408 (2010).
  18. K. Zhang, W. Zuo, Y. Chen, et al., “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,” \JournalTitleIEEE Transactions on Image Processing 26, 3142–3155 (2017).
  19. J. Gu, Z. Wang, J. Kuen, et al., “Recent advances in convolutional neural networks,” \JournalTitleCoRR abs/1512.07108 (2015).
  20. R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, “Convolutional neural networks: an overview and application in radiology,” \JournalTitleInsights into Imaging 9, 611–629 (2018).
  21. S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proceedings of the 32nd International Conference on Machine Learning, vol. 37 of Proceedings of Machine Learning Research F. Bach and D. Blei, eds. (PMLR, Lille, France, 2015), pp. 448–456.
  22. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016), pp. 770–778.
  23. S. Y. Lee, D. H. Cho, C. Jung, et al., “Denoising low-intensity diffraction signals using k𝑘kitalic_k-space deep learning: Applications to phase recovery,” \JournalTitlePhys. Rev. Res. 3, 043066 (2021).
  24. M. J. Cherukara, Y. S. G. Nashed, and R. J. Harder, “Real-time coherent diffraction inversion using deep generative networks,” \JournalTitleScientific Reports 8 (2018).
  25. L. Wu, P. Juhas, S. Yoo, and I. Robinson, “Complex imaging of phase domains by deep neural networks,” \JournalTitleIUCrJ 8, 12–21 (2021).
  26. L. Wu, S. Yoo, A. F. Suzana, et al., “Three-dimensional coherent x-ray diffraction imaging via deep convolutional neural networks,” \JournalTitlenpj Computational Materials 7 (2021).
  27. A. Tokuhisa, Y. Akinaga, K. Terayama, et al., “Single-image super-resolution improvement of x-ray single-particle diffraction images using a convolutional neural network,” \JournalTitleJournal of Chemical Information and Modeling 62, 3352–3364 (2022). PMID: 35820663.
  28. O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” (2015).
  29. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” (2014).
  30. A. F. Agarap, “Deep learning using rectified linear units (relu),” (2018).
  31. H. Yumoto, T. Koyama, A. Suzuki, et al., “High-fluence and high-gain multilayer focusing optics to enhance spatial resolution in femtosecond X-ray laser imaging,” \JournalTitleNature Communications 13, 1–8 (2022).
  32. S. Pan and Q. Yang, “A Survey on Transfer Learning,” \JournalTitleIEEE Transactions on Knowledge and Data Engineering 22, 1345–1359 (2010).
  33. D. R. Luke, “Relaxed averaged alternating reflections for diffraction imaging,” \JournalTitleInverse Problems 21, 37 (2004).
  34. S. Marchesini, H. He, H. N. Chapman, et al., “X-ray image reconstruction from a diffraction pattern alone,” \JournalTitlePhys. Rev. B 68, 140101 (2003).
  35. E. V. Formo, W. Fu, A. J. Rondinone, and S. Dai, “Utilizing AgCl:Ag and AgCl mesostructures as solid precursors in the formation of highly textured silver nanomaterials via electron-beam induced decomposition,” \JournalTitleRSC Adv. 2, 9359–9361 (2012).
  36. T. Kameshima, S. Ono, T. Kudo, et al., “Development of an X-ray pixel detector with multi-port charge-coupled device for X-ray free-electron laser experiments,” \JournalTitleReview of Scientific Instruments 85, 033110 (2014).
  37. T. Suzumura, A. Sugiki, H. Takizawa, et al., “mdx: A cloud platform for supporting data science and cross-disciplinary research collaborations,” (2022).

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