Missing Wedge Inpainting and Joint Alignment in Electron Tomography through Implicit Neural Representations
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917133701611520 |
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| author | Lim, Cedric Casert, Corneel McCray, Arthur R. C. Lee, Serin Barnum, Andrew Dionne, Jennifer Ophus, Colin |
| author_facet | Lim, Cedric Casert, Corneel McCray, Arthur R. C. Lee, Serin Barnum, Andrew Dionne, Jennifer Ophus, Colin |
| contents | Electron tomography is a powerful tool for understanding the morphology of materials in three dimensions, but conventional reconstruction algorithms typically suffer from missing-wedge artifacts and data misalignment imposed by experimental constraints. Recently proposed supervised machine-learning-enabled reconstruction methods to address these challenges rely on training data and are therefore difficult to generalize across materials systems. We propose a fully self-supervised implicit neural representation (INR) approach using a neural network as a regularizer. Our approach enables fast inline alignment through pose optimization, missing wedge inpainting, and denoising of low dose datasets via model regularization using only a single dataset. We apply our method to simulated and experimental data and show that it produces high-quality tomograms from diverse and information limited datasets. Our results show that INR-based self-supervised reconstructions offer high fidelity reconstructions with minimal user input and preprocessing, and can be readily applied to a wide variety of materials samples and experimental parameters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08113 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Missing Wedge Inpainting and Joint Alignment in Electron Tomography through Implicit Neural Representations Lim, Cedric Casert, Corneel McCray, Arthur R. C. Lee, Serin Barnum, Andrew Dionne, Jennifer Ophus, Colin Image and Video Processing Materials Science Electron tomography is a powerful tool for understanding the morphology of materials in three dimensions, but conventional reconstruction algorithms typically suffer from missing-wedge artifacts and data misalignment imposed by experimental constraints. Recently proposed supervised machine-learning-enabled reconstruction methods to address these challenges rely on training data and are therefore difficult to generalize across materials systems. We propose a fully self-supervised implicit neural representation (INR) approach using a neural network as a regularizer. Our approach enables fast inline alignment through pose optimization, missing wedge inpainting, and denoising of low dose datasets via model regularization using only a single dataset. We apply our method to simulated and experimental data and show that it produces high-quality tomograms from diverse and information limited datasets. Our results show that INR-based self-supervised reconstructions offer high fidelity reconstructions with minimal user input and preprocessing, and can be readily applied to a wide variety of materials samples and experimental parameters. |
| title | Missing Wedge Inpainting and Joint Alignment in Electron Tomography through Implicit Neural Representations |
| topic | Image and Video Processing Materials Science |
| url | https://arxiv.org/abs/2512.08113 |