Structured iterative alternating sparse matrix decomposition for thermal imaging diagnostic system

Liu, Li, Gao, Bin, Wu, Shichun, Ahmed, Junaid, Woo, Wai Lok, Li, Jianwen and Yu, Yongjie (2020) Structured iterative alternating sparse matrix decomposition for thermal imaging diagnostic system. Infrared Physics & Technology, 107. p. 103288. ISSN 1350-4495

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Official URL: https://doi.org/10.1016/j.infrared.2020.103288

Abstract

In this paper, we propose a structured iterative alternating sparse matrix decomposition to efficiently decompose the input multidimensional data from active thermography into the sum of a low-rank matrix, a sparse matrix, and a noise matrix. In particular, the sparse matrix is further factorized into a pattern constructed dictionary matrix and a coefficient matrix. The estimation of the dictionary matrix and coefficient matrix is based on integrating the vertex component analysis with the framework of the alternating direction method of multipliers. In addition, the joint structure sparsity and nonnegative constraint are emphasized as part of the learning strategy. In order to verify the effectiveness and robustness of the proposed method, experimental studies have been carried out by applying the proposed method to thermal imaging diagnostic system for carbon fiber reinforced plastics (CFRP) defects detections. The validation study has been conducted by comparing the proposed method with the current state-of-the-art algorithms. The results indicate that the proposed method significantly improves the contrast ratio between the defective regions and the non-defective regions.

Item Type: Article
Uncontrolled Keywords: Sparse decomposition, Low-rank estimation, Thermography defect detection
Subjects: F200 Materials Science
F300 Physics
G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 19 May 2020 13:28
Last Modified: 03 Jun 2020 12:45
URI: http://nrl.northumbria.ac.uk/id/eprint/43196

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