A comprehensive dataset for digital restoration of Dunhuang murals - Zishan Xu; Yuqing Yang; Qianzhen Fang; Wei Chen; Tingting Xu; Jueting Liu; Zehua Wang (Scientific Data, 2024)
Abstract The MuralDH dataset is an invaluable digital resource developed for the conservation and restoration of Dunhuang murals, which are critical components of global cultural heritage facing threats from degradation. This dataset comprises over 5000 high-resolution images tailored to 512 × 512 pixels, emphasizing the preservation of mural integrity and detail. It includes 1000 images with pixel-level damage annotations for segmentation research and 500 images specially processed for super-resolution studies, catering to a wide range of digital restoration needs. While the primary focus of this work is the dataset itself, we also introduce a supportive digital restoration framework. This framework, which encompasses damage segmentation, inpainting, and super-resolution techniques, serves as a secondary validation of MuralDH’s utility and versatility. Through MuralDH, technology revives ancient art, embodying the essence of interdisciplinary innovation. By facilitating advanced research in computer vision and artificial intelligence, MuralDH aims to revolutionize the digital preservation practices for murals and other cultural artifacts, demonstrating the critical role of interdisciplinary collaboration in safeguarding our cultural legacy.