Deep-learning-based ring artifact correction for tomographic reconstruction

This study proposes a new ring artifact correction method based on the residual neural network (ResNet) for X-ray tomography. The artifact correction network uses complementary information of each wavelet coefficient and a residual mechanism of the residual block to obtain high-precision artifacts through low operation costs. In addition, a regularization term is used to accurately extract stripe artifacts in sinograms, so that the network can better preserve image details while accurately separating artifacts. When applied to simulation and experimental data, the proposed method shows a good suppression of ring artifacts. To solve the problem of insufficient training data, ResNet is trained through the transfer learning strategy, which brings advantages of robustness, versatility and low computing cost.

Source: Journal of Synchrotron Radiation - March 10, 2023 Category: Physics Authors: Fu, T. Wang, Y. Zhang, K. Zhang, J. Wang, S. Huang, W. Wang, Y. Yao, C. Zhou, C. Yuan, Q. Tags: ring artifact correction X-ray tomography deep learning residual neural network research papers Source Type: research