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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2511.17312 |
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| _version_ | 1866911278784577536 |
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| author | Tulin, Israt Jahan Starke, Sebastian Windisch, Dominic Bieberle, André Steinbach, Peter |
| author_facet | Tulin, Israt Jahan Starke, Sebastian Windisch, Dominic Bieberle, André Steinbach, Peter |
| contents | Ultrafast electron beam X-ray computed tomography produces noisy data due to short measurement times, causing reconstruction artifacts and limiting overall image quality. To counteract these issues, two self-supervised deep learning methods for denoising of raw detector data were investigated and compared against a non-learning based denoising method. We found that the application of the deep-learning-based methods was able to enhance signal-to-noise ratios in the detector data and also led to consistent improvements of the reconstructed images, outperforming the non-learning based method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_17312 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Self-supervised denoising of raw tomography detector data for improved image reconstruction Tulin, Israt Jahan Starke, Sebastian Windisch, Dominic Bieberle, André Steinbach, Peter Machine Learning Ultrafast electron beam X-ray computed tomography produces noisy data due to short measurement times, causing reconstruction artifacts and limiting overall image quality. To counteract these issues, two self-supervised deep learning methods for denoising of raw detector data were investigated and compared against a non-learning based denoising method. We found that the application of the deep-learning-based methods was able to enhance signal-to-noise ratios in the detector data and also led to consistent improvements of the reconstructed images, outperforming the non-learning based method. |
| title | Self-supervised denoising of raw tomography detector data for improved image reconstruction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.17312 |