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In Cultural Heritage, non-invasive infrared imaging techniques are used toanalyse portions of deep structures behind wall paintings. When mosaicked,these images usually suffer from light inhomogeneities due to the experimentalsetup, which may prevent restorers from distinguishing the physical propertiesof the object under restoration. A light-balanced image is therefore essentialfor inter-frame comparisons, while preserving intra-frames details. In thispaper we apply the image osmosis model proposed in (Weickert, 2013) to solvethe light balance problem in Thermal-Quasi Reflectography (TQR) imaging. Due tothe large amount of image data, the computation of the numerical solution ofthe model may be prohibitively costly. To overcome this issue, we make use ofefficient operator splitting techniques. We test the proposed numerical schemeson the TQR measurement dataset of the mural painting "Monocromo" by Leonardo DaVinci at Castello Sforzesco (Milan, Italy). The light corrected result isregistered to a visible orthophoto, which makes it re-usable for furtherrestorations.

Efficient Osmosis Filtering of Thermal-Quasi Reflectography Images for Cultural Heritage