DOI: 10.5061/dryad.bnzs7h4jd
title: A comprehensive dataset for digital restoration of Dunhuang murals

description: 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 512x512 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.
description: # A comprehensive dataset for digital restoration of Dunhuang murals
 [https://doi.org/10.5061/dryad.bnzs7h4jd](https://doi.org/10.5061/dryad.bnzs7h4jd) The MuralDH dataset is a comprehensive collection of high-quality images for the digital restoration of Dunhuang murals. It includes over 5000 pre-processed images, curated to support research in digital art restoration, computer vision, and cultural heritage preservation. This dataset is divided into segments including damaged mural segmentation, high-resolution mural images, and images processed for super-resolution studies. The collection, designed to assist in the development and testing of digital restoration algorithms, aims to bridge traditional art with modern technology, ensuring the longevity and accessibility of these invaluable cultural treasures. ## Description of the data and file structure The dataset is structured as follows: * **Damaged Mural Segmentation Dataset:** 1000 images annotated for specific types of damage such as cracks, flaking, and fading. Each file is named according to its specific damage type and contains annotations at the pixel level. * **High-Quality Mural Images:** 500 images, each prepared for super-resolution processing. These are lower-resolution images that have been downscaled from the original high-resolution scans. * **Super-Resolution Dataset:** A subset of the High-Quality Mural Images that have been further processed for super-resolution studies. Each image file is stored in PNG format, ensuring high-quality, lossless compression. Files are organized in folders corresponding to their dataset segment, and filenames follow a consistent naming convention to indicate their content and purpose. Missing data or incomplete images are marked with a specific code (e.g., NA for not available) in the accompanying metadata file. This file also provides a detailed description of each image, including its original location, the period it depicts, and any relevant historical or cultural notes. ## Sharing/Access information The MuralDH dataset is hosted on Dryad but can also be accessed through the following link for direct download and further information: * **Access Link:** [Https://github.com/tearsheaven/MuralDH](Https://github.com/tearsheaven/MuralDH) ### Data Derivation Sources This dataset was compiled from various sources, including digital archives and collaborations with cultural heritage organizations. Each image has been carefully selected and processed to meet the research needs while adhering to copyright and preservation guidelines. ## Code/Software The dataset comes with a set of Python scripts for basic image processing tasks, including image resizing, format conversion, and initial analysis. These scripts were developed using Python 3.8, with dependencies on popular libraries such as NumPy for numerical operations, PIL for image processing tasks, and Matplotlib for generating visualizations.