  Monitoring the health of ancient artworks requires adequate prudence because
of the sensitive nature of these materials. Classical techniques for
identifying the development of faults rely on acoustic testing. These
techniques, being invasive, may result in causing permanent damage to the
material, especially if the material is inspected periodically. Non destructive
testing has been carried out for different materials since long. In this
regard, non-invasive systems were developed based on infrared thermometry
principle to identify the faults in artworks. The test artwork is heated and
the thermal response of the different layers is captured with the help of a
thermal infrared camera. However, prolonged heating risks overheating and thus
causing damage to artworks and an alternate approach is to use pseudo-random
binary sequence excitations. The faults in the artwork, though, cannot be
detected on the captured images, especially if their strength is weak. The
weaker faults are either masked by the stronger ones, by the pictorial layer of
the artwork or by the non-uniform heating. This work addresses the detection
and localization of the faults through a wavelet based subspace decomposition
scheme. The proposed scheme, on one hand, allows to remove the background
while, on the other hand, removes the undesired high frequency noise. It is
shown that the detection parameter is proportional to the diameter and the
depth of the fault. A criterion is proposed to select the optimal wavelet basis
along with suitable level selection for wavelet decomposition and
reconstruction. The proposed approach is tested on a laboratory developed test
sample with known fault locations and dimensions as well as real artworks. A
comparison with a previously reported method demonstrates the efficacy of the
proposed approach for fault detection in artworks.
