Unsupervised Enhancement of Ultrasonic Sequential Images Based on High-Frequency Feature Extraction and Bi-ConvLSTM
Author:
Funder
Joint Funds of the National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Mechanical Engineering,Mechanics of Materials
Link
https://link.springer.com/content/pdf/10.1007/s10921-023-01013-0.pdf
Reference40 articles.
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3. Nsengiyumva, W., Zhong, S., Lin, J., et al.: Advances, limitations and prospects of nondestructive testing and evaluation of thick composites and sandwich structures: a state-of-the-art review. Compos. Struct. 256, 112951 (2021). https://doi.org/10.1016/j.compstruct.2020.112951
4. Li, M., Li, X., Gao, C., et al.: Acoustic microscopy signal processing method for detecting near-surface defects in metal materials. NDT E Int. 103, 130–144 (2019). https://doi.org/10.1016/j.ndteint.2019.02.005
5. Li, X., Li, X., Liang, W., et al.: ℓ0-norm regularized minimum entropy deconvolution for ultrasonic NDT & E. NDT E Int. 47, 80–87 (2012). https://doi.org/10.1016/j.ndteint.2011.12.005
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