Inpainting of Ring Artifacts on Microtomographic Images by 3D CNN

Abstract

Ring artifacts are inevitable in microtomographic images. In Digital Rock workflow such defects might affect subsequent segmentation and flow simulation. We propose correction of ring artifacts in reconstructed microtomographic image by inpainting. Our blind inpainting method uses 3D convolutional network U-net. For the creation of training and validation datasets, we suggest an algorithm for transferring real ring artifacts to an arbitrary place of the undistorted slices of 8 big images of sandstones and sand. Parameters of the deep neural network and loss functions are analyzed. Loss function based on multi-scale structural similarity index (MS-SSIM) allows to achieve the best performance. Developed solution corrects ring artifacts perfectly from point of view visual assessment and outperforms existing inpainting methods according to quality metrics based on MS-SSIM and mean absolute error (MAE).

Keywords

• inpainting
• ring artifacts
• microtomographic image
• convolutional neural networks
• dataset creation
• 3d u-net