AI ‘Mask’ Restores Damaged Paintings in Hours, Revolutionizing Art Conservation
Art restoration, a meticulous and time-consuming process, has long relied on the steady hands and discerning eyes of conservators. For centuries, they have painstakingly identified areas needing repair and mixed precise shades to fill in each tiny region. Restoring a single painting can take weeks, months, or even over a decade.
Now, a groundbreaking development promises to transform the field. Alex Kachkine, a mechanical engineering graduate student at MIT, has unveiled a new method to physically apply digital restoration directly onto an original painting. This innovative approach, detailed in a paper published in Nature, utilizes an AI-generated “mask” to restore damaged artwork in a fraction of the time.
The restoration is printed on a very thin polymer film, forming a mask that can be precisely aligned and adhered to the original painting. Crucially, it can also be easily removed, preserving the integrity of the artwork. According to Kachkine, the digital file of the mask can be stored for future reference, providing conservators with a clear record of the restoration process.
“Because there’s a digital record of what mask was used, in 100 years, the next time someone is working with this, they’ll have an extremely clear understanding of what was done to the painting,” Kachkine explains. “And that’s never really been possible in conservation before.”
In a demonstration, Kachkine applied his method to a heavily damaged 15th-century oil painting. The AI automatically identified 5,612 separate regions requiring repair and filled them with 57,314 different colors. The entire process took just 3.5 hours, a staggering 66 times faster than traditional restoration methods, according to his estimates.
The process started as a side project. Kachkine realized that the art on display in galleries is only a fraction of the works that galleries hold, with a lot of art stored away because the works are aged or damaged, and take time to properly restore.
The new method involves first using traditional techniques to clean a painting and remove any past restoration efforts. He then scanned the cleaned painting and used existing artificial intelligence algorithms to analyze the scan and create a virtual version of what the painting likely looked like in its original state. Kachkine developed software that creates a map of regions on the original painting that require infilling, along with the exact colors needed to match the digitally restored version. This map is then translated into a physical, two-layer mask that is printed onto thin polymer-based films.
The printed films are made from materials that can be easily dissolved with conservation-grade solutions, in case conservators need to reveal the original, damaged work. The digital file of the mask can also be saved as a detailed record of what was restored.
While acknowledging the ethical considerations inherent in any restoration project, Kachkine emphasizes the importance of consulting with conservators who possess knowledge of the painting’s history and origins. He hopes that his method will make it possible to restore and display more of the damaged art currently hidden away in storage. “There is a lot of damaged art in storage that might never be seen,” Kachkine says. “Hopefully with this new method, there’s a chance we’ll see more art, which I would be delighted by.”
