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- W3022381609 abstract "Ring artifacts are inevitable in microtomographic images. In a Digital Rock workflow, such defects might affect the subsequent segmentation and flow simulation. We propose a correction of ring artifacts in reconstructed microtomographic images by inpainting. Our blind inpainting method uses a 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 in the undistorted slices of 8 big images of sandstones and sand. The parameters of the deep neural network and loss functions are analyzed. A loss function based on the multi-scale structural similarity index (MS-SSIM) allows to achieve the best performance. The developed solution corrects ring artifacts perfectly from a point of view of visual assessment and outperforms existing inpainting methods according to quality metrics based on MS-SSIM and mean absolute error (MAE)." @default.
- W3022381609 created "2020-05-13" @default.
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- W3022381609 date "2020-04-01" @default.
- W3022381609 modified "2023-09-23" @default.
- W3022381609 title "Inpainting of Ring Artifacts on Microtomographic Images by 3D CNN" @default.
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- W3022381609 doi "https://doi.org/10.23919/fruct48808.2020.9087422" @default.
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