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.

翻译：基于深度神经网络的图像重建方法取得了前所未有的成功，彻底改变了多个应用学科的处理与分析范式。在数字人文学科领域，古壁画的数字重建任务因年代久远、风化磨损及人为修饰导致的可用训练数据匮乏而面临特殊挑战。为克服这些困难，我们采用深度图像先验（Deep Image Prior, DIP）修复方法，该方法通过逐步更新未训练的卷积神经网络，使网络在匹配图像中可靠信息的同时，在其他区域施加正则化约束，从而生成合适的重建结果。与现有主流方法（基于变分/偏微分方程及基于块的方法）相比，基于DIP的修复能减少伪影、更好地适应上下文/非局部信息，为艺术史研究者提供了富有价值的有效工具。作为案例研究，我们将该方法应用于地中海阿尔卑斯弧地区多座教堂中严重受损的中世纪绘画数字图像数据集，重建缺失图像内容，并详细阐述了如何融合可见光与不可见光（如红外）信息，以识别并重建受损图像区域。