{"ID":2831894,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.08113","arxiv_id":"2512.08113","title":"Missing Wedge Inpainting and Joint Alignment in Electron Tomography through Implicit Neural Representations","abstract":"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.","short_abstract":"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 reconstructi...","url_abs":"https://arxiv.org/abs/2512.08113","url_pdf":"https://arxiv.org/pdf/2512.08113v1","authors":"[\"Cedric Lim\",\"Corneel Casert\",\"Arthur R. C. McCray\",\"Serin Lee\",\"Andrew Barnum\",\"Jennifer Dionne\",\"Colin Ophus\"]","published":"2025-12-08T23:36:48Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cond-mat.mtrl-sci\"]","methods":"[]","has_code":false}