Self-supervised denoising of raw tomography detector data for improved image reconstruction
This addresses image quality issues in medical or industrial tomography, but it is incremental as it builds on existing denoising approaches.
The paper tackled noise in raw tomography detector data from ultrafast electron beam X-ray CT by investigating two self-supervised deep learning denoising methods, which improved signal-to-noise ratios and outperformed a non-learning method in image reconstruction.
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.