On the Power Spectral Density Applied to the Analysis of Old Canvases

TL;DR

This paper introduces a robust power spectral density-based framework for analyzing old canvas radiographs, enabling accurate fabric characterization and comparison even in deteriorated artworks where previous methods struggle.

Contribution

It presents a new theoretical and practical approach using PSD to analyze and compare old canvases, improving robustness over prior techniques.

Findings

Accurate thread counting in old paintings.

Effective comparison of canvases using PSD fingerprints.

Applicable to deteriorated artworks where previous methods fail.

Abstract

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.