{"ID":2838647,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.17312","arxiv_id":"2511.17312","title":"Self-supervised denoising of raw tomography detector data for improved image reconstruction","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2511.17312","url_pdf":"https://arxiv.org/pdf/2511.17312v1","authors":"[\"Israt Jahan Tulin\",\"Sebastian Starke\",\"Dominic Windisch\",\"André Bieberle\",\"Peter Steinbach\"]","published":"2025-11-21T15:30:44Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}