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

TL;DR

This paper introduces self-supervised deep learning techniques to denoise raw tomography detector data, significantly improving image quality and signal-to-noise ratios in ultrafast electron beam X-ray computed tomography.

Contribution

It presents novel self-supervised deep learning methods for denoising raw detector data, outperforming traditional non-learning approaches in tomography image reconstruction.

Findings

Deep learning methods enhanced signal-to-noise ratios.

Reconstructed images showed consistent quality improvements.

Outperformed non-learning denoising methods.

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.