A Comparative Study of Supervised and Self-Supervised Denoising Techniques for Defect Segmentation in Industrial CT Imaging
X-ray computed tomography (CT) is a powerful imaging tool for defect detection, segmentation and feature extraction in industrial applications as it enables non-destructive evaluation. The presence of artifacts and noise, however, imposes difficulties on the defect detection due to low contrast bet...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | deu |
| Published: |
NDT.net
2025-02-01
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| Series: | e-Journal of Nondestructive Testing |
| Online Access: | https://www.ndt.net/search/docs.php3?id=30735 |
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| Summary: | X-ray computed tomography (CT) is a powerful imaging tool for defect detection, segmentation and feature extraction in industrial applications as it enables non-destructive evaluation. The presence of artifacts and noise, however, imposes difficulties on the defect detection due to low contrast between defects and material. Different methods, the most recent ones based on deep learning, have been proposed to address both CT artifacts and noise. In this work, three state-of-the-art denoising techniques, namely BM3D, a supervised UNet and Noise2Inverse are compared with a novel approach based on a set of CNNs applied at different stages of the CT data acquisition pipeline. The comparison is done regarding the capabilities of the methods in terms of image quality enhancement as a preprocessing step in CT-based data analysis before performing defect segmentation.
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| ISSN: | 1435-4934 |