Combined multiphoton fluorescence microscopy and photoacoustic imaging for stratigraphic analysis of paintings

We demonstrate the effective combination of multiphoton and photoacoustic (PA) imaging for the high-resolution stratigraphic analysis of multilayered art objects with emphasis on paintings. A novel convolution-based algorithm is additionally applied for the precise discrimination of nonlinear signals, providing valuable information in regard to the thickness and composition of successive varnish and paint layers in the mock-up samples. On the other hand, PA contrast complements the extracted data by revealing well-hidden graphite underdrawings below the paint at high sensitivity levels. The final composite images are directly compared with cross-sectional brightfield observations, validating the capabilities of the bimodal diagnosis in terms of measurement accuracy and contrast specificity. The presented hybrid diagnostic approach has the potential to optimize delicate interventions in works of art such as the selective removal of aged materials, thus promoting a significantly improved restoration outcome.