Enhanced CFRP Defect Detection From Highly Undersampled Thermographic Data via Low-Rank Tensor Completion-Based Thermography
Author:
Affiliation:
1. Jiangsu Key Laboratory of Design and Manufacturing of Micro-Nano Biomedical Instruments, School of Mechanical Engineering, Southeast University, Nanjing, China
2. School of Science, Nanjing University of Science and Technology, Nanjing, China
Funder
National Natural Science Foundation of China
Key R&D Program of Jiangsu Province of China
Scientific Research Foundation of Graduate School of Southeast University
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/9424/9906876/09722967.pdf?arnumber=9722967
Reference50 articles.
1. Low-rank tensor completion via smooth matrix factorization
2. Matrix factorization for low-rank tensor completion using framelet prior
3. Ensemble Joint Sparse Low-Rank Matrix Decomposition for Thermography Diagnosis System
4. Structured iterative alternating sparse matrix decomposition for thermal imaging diagnostic system
5. Sparse Low-Rank Tensor Decomposition for Metal Defect Detection Using Thermographic Imaging Diagnostics
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