On the Power Spectral Density Applied to the Analysis of Old Canvases
This provides art historians with a more robust tool for painting diagnostics like dating and attribution, though it appears incremental as it builds on existing signal processing approaches.
The authors tackled the problem of analyzing deteriorated or small-sized paintings by developing a framework using power spectral density (PSD) on X-ray images to characterize canvas fabrics, achieving accurate thread counting and enabling painting comparisons where previous methods fail.
A routine task for art historians is painting diagnostics, such as dating or attribution. Signal processing of the X-ray image of a canvas provides useful information about its fabric. However, previous methods may fail when very old and deteriorated artworks or simply canvases of small size are studied. We present a new framework to analyze and further characterize the paintings from their radiographs. First, we start from a general analysis of lattices and provide new unifying results about the theoretical spectra of weaves. Then, we use these results to infer the main structure of the fabric, like the type of weave and the thread densities. We propose a practical estimation of these theoretical results from paintings with the averaged power spectral density (PSD), which provides a more robust tool. Furthermore, we found that the PSD provides a fingerprint that characterizes the whole canvas. We search and discuss some distinctive features we may find in that fingerprint. We apply these results to several masterpieces of the 17th and 18th centuries from the Museo Nacional del Prado to show that this approach yields accurate results in thread counting and is very useful for paintings comparison, even in situations where previous methods fail.