The fact that the wing panels are painted on both sides posed a unique challenge for conventional X-ray analysis. X-rays penetrate so deeply that it can be difficult to determine which content applies to which side of the panel, since “all of the images are visibly overlaid or ‘blended’ together,” the authors wrote. So Daubechies and her colleagues developed a deep neural network algorithm to study mixed X-ray images containing features from the front and back of the painting’s double-sided panels. They successfully applied their technique to X-ray images of the Adam and Eve wing panels to deconstruct that data into two clear images — significantly improving on the performance achieved by prior methods.
While art conservationists have made good use of techniques drawn from physics and chemistry, including making X-ray and infrared images of valuable works, they have been slower to incorporate the most state-of-the-art image analysis methods. And while image processing is a vital research area within the electrical engineering and computer science disciplines, according to Daubechies, much of this work has not focused on the unique challenges related to art conservation. There are fewer images available compared to, say, identifying pictures of cats and dogs on Instagram. “Your statistical inference cannot be the same because you have a lot less data,” co-author Barak Sober told Ars.
. “These images will also help to understand the brothers Van Eyck’s techniques and the changes carried out in the course of the successive execution of this unique masterpiece.So might this new approach also prove useful for examining underpaintings, as in the case of Picasso’s The Old Guitarist
? “It’s similar yet different, because in the case of this double X-ray of the double painted panel, we had access to high-quality visible images on both sides,” Daubechies told Ars. “When you want to find things underneath another painting, the problem is different, in that you don’t have access to the one that’s hidden.” That makes it a harder problem to solve, although, since imaging that hidden painting requires much more sophisticated imaging techniques, the AI would be working with more data.“This approach demonstrates that artificial intelligence-oriented techniques — powered by deep learning — can be used to challenge solve challenges arising in art investigation, “said co-author Miguel Rodriguesof UCL. “We’d like to see the impact that the development of similar AI-oriented approaches will have on our ability to reveal other hidden features in a painting, such as earlier concealed designs.”DOI: (Science Advances) ******************************, .************************************************************** / sciadv.aaw ( (About DOIs). ********************************** (********************************************************************************************************* Read More**************************************
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