Tensor robust principal component analysis based on Bayesian Tucker decomposition for thermographic inspection

Abstract

Thermographic inspection is considered an effective and promising nondestructive testing tool because of its intuitiveness, wide range and noncontact property. Despite this, the detection of weak defects and the recovery of their shape remain difficult, particularly when the surface being inspected is the opposite of the surface being drilled. This study proposes a new tensor robust principal component analysis method based on Bayesian Tucker decomposition to improve the spatial resolution of thermography. A hierarchical form of a generalized Student-t prior is imposed on the model parameters in the Bayesian framework so as to approximate the low-rank component related to the defect feature. Through variational Bayesian inference, all model parameters are adaptively estimated. Based on two experimental data, it appears that the proposed method is capable of improving the spatial resolution and detection accuracy of the thermographic inspection system.