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 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%.

Automated summary

A deconvolutional reconstruction method based on the LR algorithm was developed, effectively suppressing random noise and blur effect caused by thermal diffusion. The proposed LR method exhibited a higher signal-to-noise ratio and successfully detected defects with a diameter of 1.5 mm and a depth of 2.0 mm.