A Deconvolutional Reconstruction Method Based on Lucy-Richardson Algorithm for Joint Scanning Laser Thermography

Joint scanning laser thermography (JLST) is well-known for its efficiency to overcome the field of view (FOV) limitation of thermal imagers. However, JSLT requires a data reconstruction to reveal the location of the defective area straightforwardly. Moreover, its detection capacity is limited by the...

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Vydané v:IEEE transactions on instrumentation and measurement Ročník 70; s. 1 - 8
Hlavní autori: He, Zhiyi, Wang, Hongjin, Li, Yiwen, Zhang, Zhenjun, Zhang, Yudong, Bi, Hanbo, He, Yunze
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Adaptive algorithms
Algorithms
Cylinders
Deconvolution algorithm
Defects
Degradation
Diffusion coating
Fast Fourier transformations
Field of view
Fourier transforms
functional coating materials
Heat flux
Image reconstruction
Image restoration
infrared thermography
Inspection
joint laser scanning
Laser excitation
Laser modes
Mathematical model
nondestructive testing (NDT) and evaluation
Principal components analysis
Random noise
Reconstruction
Scanning
Signal to noise ratio
Thermal diffusion
Thermography
ISSN:0018-9456, 1557-9662
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Popis
Shrnutí:Joint scanning laser thermography (JLST) is well-known for its efficiency to overcome the field of view (FOV) limitation of thermal imagers. However, JSLT requires a data reconstruction to reveal the location of the defective area straightforwardly. Moreover, its detection capacity is limited by the lack of a deconvolution algorithm adaptive to the reconstructed data. In this study, a deconvolutional reconstruction method based on the Lucy-Richardson (LR) algorithm has been developed for JST, which is effective in suppressing random noise and the blur effect caused by the thermal diffusion. A JSLT inspection is carried out on a functional coating material with cylinder-like defects to test the performance of the proposed method. In comparison to the directly processed method on the original data, the proposed method is processed on the reconstructed data and then compared with principal component analysis (PCA), restored pseudo heat flux (RPHF), fast Fourier transform (FFT) methods and non-negative matrix factorization (NMF). The experimental results indicated that our proposed LR method exhibited a higher signal-to-noise ratio. Besides, it can detect the cylinder-mocked debonding defects with a diameter of 1.5 mm and a depth of 2.0 mm buried under the 1.0-mm coating. In addition, the defect detection diameter-to-depth ratio reached 1.5, while the defect detection rate of the test specimens can approach 90%.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2020.3034967