Self-supervised denoising of raw tomography detector data for improved image reconstruction

• URL: http://arxiv.org/abs/2511.17312v1
• Date: Fri, 21 Nov 2025 15:30:44 GMT
• Title: Self-supervised denoising of raw tomography detector data for improved image reconstruction
• Abstract summary: Ultrafast electron beam X-ray computed tomography produces noisy data due to short measurement times.<n>To counteract these issues, two self-supervised deep learning methods for denoising of raw detector data were investigated.<n>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.
• Score: 2.91425891370655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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