Abstract
ABSTRACT This paper explores the implementation of Latent Low-Rank Representation (LatLRR) on pulsed thermographic data. LatLRR decomposes an image in the form of a linear association of three types of information: observed, unobserved and noise. This information is then used in order to separate the salient and principal features. This study has found that when used as a post-processing method prior to the application of state-of-the-art signal processing techniques, such as principal component thermography (PCT) and pulsed phase thermography (PPT), LatLRR significantly improves defect detection: 18% for PCT and 92% for PPT. Nevertheless, no noticeable improvement was measured when LatLRR was used to reconstruct a noiseless version of each image of a dataset, before processing it with a state-of-the-art algorithm. The investigations conducted on each type of feature returned by the LatLRR have also failed to provide results regarding the detection of defects.
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