Deep-learning-based image registration for nano-resolution tomographic reconstruction
Nano-resolution full-field transmission X-ray microscopy has been successfully applied to a wide range of research fields thanks to its capability of non-destructively reconstructing the 3D structure with high resolution. Due to constraints in the practical implementations, the nano-tomography data...
| Publié dans: | Journal of synchrotron radiation. - 1994. - 28(2021), Pt 6 vom: 01. Nov., Seite 1909-1915 |
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| Auteur principal: | |
| Autres auteurs: | , , , , , , , , , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2021
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| Accès à la collection: | Journal of synchrotron radiation |
| Sujets: | Journal Article deep learning full-field transmission X-ray microscopy image registration nano-tomography residual neural network |
| Résumé: | Nano-resolution full-field transmission X-ray microscopy has been successfully applied to a wide range of research fields thanks to its capability of non-destructively reconstructing the 3D structure with high resolution. Due to constraints in the practical implementations, the nano-tomography data is often associated with a random image jitter, resulting from imperfections in the hardware setup. Without a proper image registration process prior to the reconstruction, the quality of the result will be compromised. Here a deep-learning-based image jitter correction method is presented, which registers the projective images with high efficiency and accuracy, facilitating a high-quality tomographic reconstruction. This development is demonstrated and validated using synthetic and experimental datasets. The method is effective and readily applicable to a broad range of applications. Together with this paper, the source code is published and adoptions and improvements from our colleagues in this field are welcomed |
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| Description: | Date Revised 08.11.2021 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1600-5775 |
| DOI: | 10.1107/S1600577521008481 |