Ring artifacts in X-ray micro-CT images are one of the primary causes of
concern in their accurate visual interpretation and quantitative analysis. The
geometry of X-ray micro-CT scanners is similar to the medical CT machines,
except the sample is rotated with a stationary source and detector. The ring
artifacts are caused by a defect or non-linear responses in detector pixels
during the MicroCT data acquisition. Artifacts in MicroCT images can often be
so severe that the images are no longer useful for further analysis. Therefore,
it is essential to comprehend the causes of artifacts and potential solutions
to maximize image quality. This article presents a convolution neural network
(CNN)-based Deep Learning (DL) model inspired by UNet with a series of encoder
and decoder units with skip connections for removal of ring artifacts. The
proposed architecture has been evaluated using the Structural Similarity Index
Measure (SSIM) and Mean Squared Error (MSE). Additionally, the results are
compared with conventional filter-based non-ML techniques and are found to be
better than the latter.

通过使用基于卷积神经网络（CNN）的深度学习模型，本文介绍了一种受到 UNet 启发的架构，其中包括一系列的编码器、解码器单元以及跳跃连接，用于去除 X 射线微 CT 图像中的环形伪影。结果表明，与传统的基于滤波器的非机器学习技术相比，所提出的架构在消除环形伪影方面取得了更好的效果。