The unprecedented success of image reconstruction approaches based on deep neural networks has revolutionised both the processing and the analysis paradigms in several applied disciplines. In the field of digital humanities, the task of digital reconstruction of ancient frescoes is particularly challenging due to the scarce amount of available training data caused by ageing, wear, tear and retouching over time. To overcome these difficulties, we consider the Deep Image Prior (DIP) inpainting approach which computes appropriate reconstructions by relying on the progressive updating of an untrained convolutional neural network so as to match the reliable piece of information in the image at hand while promoting regularisation elsewhere. In comparison with state-of-the-art approaches (based on variational/PDEs and patch-based methods), DIP-based inpainting reduces artefacts and better adapts to contextual/non-local information, thus providing a valuable and effective tool for art historians. As a case study, we apply such approach to reconstruct missing image contents in a dataset of highly damaged digital images of medieval paintings located into several chapels in the Mediterranean Alpine Arc and provide a detailed description on how visible and invisible (e.g., infrared) information can be integrated for identifying and reconstructing damaged image regions.

In a few horrible hours, Pompeii was turned from a vibrant city into an ash-embalmed wasteland, smothered by a furious volcanic eruption in A.D. 79. Then in this century, the excavated Roman city appeared alarmingly close to a second death, assailed by decades of neglect, mismanagement and scant systematic maintenance of the heavily visited ruins. The 2010 collapse of a hall where gladiators trained nearly cost Pompeii its coveted UNESCO World Heritage Site designation. But these days, Pompeii is experiencing the makings of a rebirth. Excavations undertaken as part of engineering stabilization strategies to prevent new collapses are yielding a raft of revelations about the everyday lives of Pompeii's residents, as the lens of social class analysis is increasingly applied to new discoveries.

When we sit down to solve a jigsaw puzzle, there's always one thing we take for granted: the picture on the box. Without that point of reference, we'd be pulling our hair out, trying and failing to rebuild a jumbled pile of miscellaneous pieces. That's exactly what was happening in the ancient Roman city of Pompeii, where over 10,000 fragmented pieces from 2,000-year-old frescoes have been lying around for decades, waiting for someone to solve the puzzle. Now, a team of scientists led by the Venice-based Italian Institute of Technology may have found a solution: train a robot to do it. A key part of the project will be teaching the algorithm how to study like an archeologist and think like a puzzle master.

Imagine you have a jigsaw puzzle with 10,000 pieces but no picture on a box. In fact, you don't even have the box--it was destroyed nearly 2,000 years ago. These puzzle pieces are fragments of frescoes in the ancient Roman city of Pompeii that were leveled or buried by the eruption of Mount Vesuvius in A.D. 79. Some pieces are missing, others are broken. And instead of being precisely-cut shapes designed to neatly interlock, they're damaged, irregular fragments.