X-rays are a well-established tool to help analyze and restore valuable paintings, because their higher frequency means they pass right through paintings without harming them. X-ray imaging can reveal anything that has been painted over a canvas, or where the artist may have altered his (or her) original vision. But the technique has its limitations, and that's where machine learning can prove useful. Two papers this fall illustrated the use of AI to solve specific problems in art analysis and conservation: one to reconstruct an underpainting in greater detail, and the other to make it easier to image two-sided painted panels.
Picasso's The Old Guitarist is one of the best known works from the artist's so-called "Blue Period." Two decades ago, x-ray and infrared analysis revealed that he had re-used an older canvas (a common practice for struggling artists of the period). There was another painting underneath, of a seated woman, that matched a sketch Picasso had included in a letter to a friend. But the x-ray and infrared images couldn't provide sufficient detail to get a sense of what the original painting really looked like, especially the choice of colors.
In a paper posted to the physics arXiv in September, Anthony Bourached and George Cann of the University College London described how they employed machine learning to reconstruct a full-color image of Picasso's original underpainting—specifically, a technique called neural style transfer, originally developed a few years ago by researchers at the University of Tübingen in Germany. Per Technology Review:
Source: Ars Technica