Numerous cultural assets host a great historical and moral value, but due to their degradation, this value is heavily affected as their attractiveness is lost. One of the solutions that most heritage organizations and museums currently choose is to leverage the knowledge of art and history experts in addition to curators to recover and restore the damaged assets. This process is labor-intensive, expensive and more often results in just an assumption over the damaged or missing region. In this work, we tackle the issue of completing missing regions in artwork through advanced deep learning and image reconstruction (inpainting) techniques. Following our analysis of different image completion and reconstruction approaches, we noticed that these methods suffer from various limitations such as lengthy processing times and hard generalization when trained with multiple visual contexts. Most of the existing learning-based image completion and reconstruction techniques are trained on large datasets with the objective of retrieving the original data distribution of the training samples. However, this distribution becomes more complex when the training data is diverse making the training process difficult and the reconstruction inefficient. Through this paper, we present a clustering-based low-delay image completion and reconstruction approach which combines supervised and unsupervised learning to address the highlighted issues. We compare our technique to the current state of the art using a real-world dataset of artwork collected from various cultural institutions. Our approach is evaluated using statistical methods and a surveyed audience to better interpret our results objectively and subjectively.

许多文化资产具有重大的历史和道德价值，但由于其退化，由于失去了吸引力，该价值受到严重影响。当前，大多数遗产组织和博物馆选择的解决方案之一是，除了策展人之外，还利用艺术和历史专家的知识来恢复和恢复受损的资产。该过程是劳动密集型的，昂贵的并且更经常导致仅对损坏或丢失的区域进行假设。在这项工作中，我们通过高级深度学习和图像重建（修复）技术解决了完成艺术品中缺失区域的问题。在我们分析了不同的图像完成和重建方法之后，我们注意到这些方法在多种视觉环境下训练时受到各种限制，例如处理时间长和泛化困难。大多数现有的基于学习的图像完成和重建技术都是在大型数据集上进行训练的，目的是检索训练样本的原始数据分布。然而，当训练数据多样时，这种分布变得更加复杂，使得训练过程困难并且重建效率低下。通过本文，我们提出了一种基于聚类的低延迟图像完成和重构方法，该方法结合了有监督的学习和无监督的学习来解决突出的问题。我们使用从各种文化机构收集的艺术品的真实世界数据集，将我们的技术与当前的技术水平进行比较。