Active IR thermography processing based on Higher Order Statistics for nondestructive evaluation

Active infrared thermography is a nondestructive method for evaluating defects in artworks. A conventional excitation radiation heats the sample and the photothermal response is recorded by an infrared (IR) camera. Classical pulsed excitation has shown the feasibility of such a detection system, but the energy deposition for a long period of time can alter samples. Random excitations can prevent such problem, but signal processing methods should be implemented to extract the useful information. We propose a processing method that combines Singular Value Decomposition (SVD) and Higher Order Statistics (HOS). The former decomposes the dataset in several subspaces, allowing to remove the influence of the acquisition environment and system. The latter is used to build up from the useful information one or two images for diagnostic. We show on a mural-type "laboratory" and on a in situ artwork that this method allows good identification of defects, providing a complementary detector to classical analysis.