TY - CONF A1 - Tsamos, Athanasios T1 - Denoising, Deblurring and Automatic Segmentation of XCT Data with Deep Learning and Synthetic XCT Training Data. A Case Study on Al-Si MMCs. N2 - We employ in-house generated synthetic Al-Si matrix composite XCT data for training deep convolutional neural networks for XCT data conditioning and automatic segmentation. We propose an in-house multilevel deep conditioning framework capable of rectifying noise and blur in corrupted XCT data sequentially. Furthermore, for automatic segmentation, we utilize a special in-house network coupled with a novel iterative segmentation algorithm capable of generalized learning from synthetic data. We report a consistent SSIM efficiency of 92%, 99%, and 95% for the combined denoising/deblurring, standalone denoising, and standalone deblurring, respectively. The overall segmentation precision was over 85% according to the Dice coefficient. We used experimental XCT data from various scans of Al-Si matrix composites reinforced with ceramic particles and fibers. T2 - 12th International Conference on Industrial Computed Tomography (iCT2023) CY - Fürth, Germany DA - 27.02.2023 KW - Automatic Segmentation KW - Denoising Deblurring Sharpening KW - Artificial Intelligence KW - DCNNs KW - Synthetic Training Data KW - XCT PY - 2023 AN - OPUS4-59095 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -