Deep Learning for Spectroscopic X‐ray Nano‐Imaging Denoising
Synchrotron transmission X‐ray microscopy with absorption near edge structure (TXM‐XANES) is a powerful tool for investigating the structure and composition of materials at nano‐ to meso‐scales. It is, however, often challenged by high levels of noise that obscure critical details at the single‐pixe...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Advanced Intelligent Systems |
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| Online Access: | https://doi.org/10.1002/aisy.202400318 |
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| author | Tianyu Fu Kai Zhang Qingxi Yuan Jizhou Li Piero Pianetta Yijin Liu |
| author_facet | Tianyu Fu Kai Zhang Qingxi Yuan Jizhou Li Piero Pianetta Yijin Liu |
| author_sort | Tianyu Fu |
| collection | DOAJ |
| description | Synchrotron transmission X‐ray microscopy with absorption near edge structure (TXM‐XANES) is a powerful tool for investigating the structure and composition of materials at nano‐ to meso‐scales. It is, however, often challenged by high levels of noise that obscure critical details at the single‐pixel level. To address this issue, a deep learning‐based algorithm is developed for suppressing the image noise, grounded in self‐supervised learning principles. In contrast to traditional image denoising methods, this approach successfully enhances the visibility of fine details while significantly reducing the noise in the X‐ray images. Through this advancement, the potential of the approach for improving the accuracy and interpretability of the TXM‐XANES data is demonstrated, thereby enabling more precise detection of nanoscale phenomena such as inhomogeneous cation redox and metal segregation in battery cathode materials. This technique offers an effective new avenue for harnessing the full potential of synchrotron TXM‐XANES imaging, paving the way for a range of exciting new studies in materials science and beyond. |
| format | Article |
| id | doaj-art-8ba3f28cce794e9da1c1b4a408f61755 |
| institution | Kabale University |
| issn | 2640-4567 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-8ba3f28cce794e9da1c1b4a408f617552025-01-21T07:26:27ZengWileyAdvanced Intelligent Systems2640-45672025-01-0171n/an/a10.1002/aisy.202400318Deep Learning for Spectroscopic X‐ray Nano‐Imaging DenoisingTianyu Fu0Kai Zhang1Qingxi Yuan2Jizhou Li3Piero Pianetta4Yijin Liu5Beijing Synchrotron Radiation Facility X‐ray Optics and Technology Laboratory Institute of High Energy Physics Chinese Academy of Sciences Yuquan Road, Shijingshan District Beijing 100043 ChinaBeijing Synchrotron Radiation Facility X‐ray Optics and Technology Laboratory Institute of High Energy Physics Chinese Academy of Sciences Yuquan Road, Shijingshan District Beijing 100043 ChinaBeijing Synchrotron Radiation Facility X‐ray Optics and Technology Laboratory Institute of High Energy Physics Chinese Academy of Sciences Yuquan Road, Shijingshan District Beijing 100043 ChinaSchool of Data Science City University of Hong Kong Kowloon Tong Hong Kong 999077 ChinaStanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory Menlo Park CA 94025 USAWalker Department of Mechanical Engineering University of Texas at Austin Austin TX 78712 USASynchrotron transmission X‐ray microscopy with absorption near edge structure (TXM‐XANES) is a powerful tool for investigating the structure and composition of materials at nano‐ to meso‐scales. It is, however, often challenged by high levels of noise that obscure critical details at the single‐pixel level. To address this issue, a deep learning‐based algorithm is developed for suppressing the image noise, grounded in self‐supervised learning principles. In contrast to traditional image denoising methods, this approach successfully enhances the visibility of fine details while significantly reducing the noise in the X‐ray images. Through this advancement, the potential of the approach for improving the accuracy and interpretability of the TXM‐XANES data is demonstrated, thereby enabling more precise detection of nanoscale phenomena such as inhomogeneous cation redox and metal segregation in battery cathode materials. This technique offers an effective new avenue for harnessing the full potential of synchrotron TXM‐XANES imaging, paving the way for a range of exciting new studies in materials science and beyond.https://doi.org/10.1002/aisy.202400318battery materials characterizationinhomogeneous cation redoxTXM‐XANESunsupervised image denoisingX‐ray imaging |
| spellingShingle | Tianyu Fu Kai Zhang Qingxi Yuan Jizhou Li Piero Pianetta Yijin Liu Deep Learning for Spectroscopic X‐ray Nano‐Imaging Denoising Advanced Intelligent Systems battery materials characterization inhomogeneous cation redox TXM‐XANES unsupervised image denoising X‐ray imaging |
| title | Deep Learning for Spectroscopic X‐ray Nano‐Imaging Denoising |
| title_full | Deep Learning for Spectroscopic X‐ray Nano‐Imaging Denoising |
| title_fullStr | Deep Learning for Spectroscopic X‐ray Nano‐Imaging Denoising |
| title_full_unstemmed | Deep Learning for Spectroscopic X‐ray Nano‐Imaging Denoising |
| title_short | Deep Learning for Spectroscopic X‐ray Nano‐Imaging Denoising |
| title_sort | deep learning for spectroscopic x ray nano imaging denoising |
| topic | battery materials characterization inhomogeneous cation redox TXM‐XANES unsupervised image denoising X‐ray imaging |
| url | https://doi.org/10.1002/aisy.202400318 |
| work_keys_str_mv | AT tianyufu deeplearningforspectroscopicxraynanoimagingdenoising AT kaizhang deeplearningforspectroscopicxraynanoimagingdenoising AT qingxiyuan deeplearningforspectroscopicxraynanoimagingdenoising AT jizhouli deeplearningforspectroscopicxraynanoimagingdenoising AT pieropianetta deeplearningforspectroscopicxraynanoimagingdenoising AT yijinliu deeplearningforspectroscopicxraynanoimagingdenoising |