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- W4310350213 abstract "Miralon sheets are an advanced carbon-based product which enables environmentally resistant solutions for some of the toughest industrial problems. One of the challenges for quality control is to ensure the uniformity of density distribution. However, current areal density maps obtained from a beta transmission equipment are insufficient to identify variation and defects at the required level. As areal density maps can be interpreted as images, image super-resolution convolutional neural networks (CNNs) promise to reconstruct high-resolution areal density maps with fine texture details. However, for this promise to be realized two challenges need to be overcome. First, generating areal density maps is expensive, and only limited training data is available. Second, current 2D CNNs cannot provide sufficient accuracy in depth. Therefore, we propose a 3-layer voxel representation to adapt 2D CNNs to the particular application of Miralon sheets. Moreover, we also study the transform domains to provide more distinguishable features for density distribution patterns. Our results demonstrate that appropriate data augmentation is essential to achieving accurate results and transform domains could provide special information based on image contents. Using our proposed techniques, a high-resolution areal density map, which is rich in 3D information, can be obtained by leveraging 2D image super-resolution CNNs." @default.
- W4310350213 created "2022-12-09" @default.
- W4310350213 creator A5027655988 @default.
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- W4310350213 date "2022-11-26" @default.
- W4310350213 modified "2023-09-25" @default.
- W4310350213 title "Multi-layer Wavelet Transformations for Image Super-Resolution: Applications to Voxel-Based Deep Learning and Areal Density Maps of Carbon Nanotube Sheets" @default.
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- W4310350213 doi "https://doi.org/10.1007/978-981-19-6153-3_14" @default.
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