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Monitoring the health of ancient artworks requires adequate prudence becauseof the sensitive nature of these materials. Classical techniques foridentifying the development of faults rely on acoustic testing. Thesetechniques, being invasive, may result in causing permanent damage to thematerial, especially if the material is inspected periodically. Non destructivetesting has been carried out for different materials since long. In thisregard, non-invasive systems were developed based on infrared thermometryprinciple to identify the faults in artworks. The test artwork is heated andthe thermal response of the different layers is captured with the help of athermal infrared camera. However, prolonged heating risks overheating and thuscausing damage to artworks and an alternate approach is to use pseudo-randombinary sequence excitations. The faults in the artwork, though, cannot bedetected on the captured images, especially if their strength is weak. Theweaker faults are either masked by the stronger ones, by the pictorial layer ofthe artwork or by the non-uniform heating. This work addresses the detectionand localization of the faults through a wavelet based subspace decompositionscheme. The proposed scheme, on one hand, allows to remove the backgroundwhile, on the other hand, removes the undesired high frequency noise. It isshown that the detection parameter is proportional to the diameter and thedepth of the fault. A criterion is proposed to select the optimal wavelet basisalong with suitable level selection for wavelet decomposition andreconstruction. The proposed approach is tested on a laboratory developed testsample with known fault locations and dimensions as well as real artworks. Acomparison with a previously reported method demonstrates the efficacy of theproposed approach for fault detection in artworks.

Wavelet subspace decomposition of thermal infrared images for defect detection in artworks