Zishan Xu;Xiaofeng Zhang;Wei Chen;Jueting Liu;Tingting Xu;Zehua Wang
{"title":"MuralDiff: Diffusion for Ancient Murals Restoration on Large-Scale Pre-Training","authors":"Zishan Xu;Xiaofeng Zhang;Wei Chen;Jueting Liu;Tingting Xu;Zehua Wang","doi":"10.1109/TETCI.2024.3359038","DOIUrl":null,"url":null,"abstract":"This paper focuses on the crack detection and digital restoration of ancient mural cultural heritage, proposing a comprehensive method that combines the Unet network structure and diffusion model. Firstly, the Unet network structure is used for efficient crack detection in murals by constructing an ancient mural image dataset for training and validation, achieving accurate identification of mural cracks. Next, an edge-guided optimized masking strategy is adopted for mural restoration, effectively preserving the information of the murals and reducing the damage to the original murals during the restoration process. Lastly, a diffusion model is employed for digital restoration of murals, improving the restoration performance by adjusting parameters to achieve natural repair of mural cracks. Experimental results show that comprehensive method based on the Unet network and diffusion model has significant advantages in the tasks of crack detection and digital restoration of murals, providing a novel and effective approach for the protection and restoration of ancient murals. In addition, this research has significant implications for the technological development in the field of mural restoration and cultural heritage preservation, contributing to the advancement and technological innovation in related fields.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Emerging Topics in Computational Intelligence","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10440368/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
This paper focuses on the crack detection and digital restoration of ancient mural cultural heritage, proposing a comprehensive method that combines the Unet network structure and diffusion model. Firstly, the Unet network structure is used for efficient crack detection in murals by constructing an ancient mural image dataset for training and validation, achieving accurate identification of mural cracks. Next, an edge-guided optimized masking strategy is adopted for mural restoration, effectively preserving the information of the murals and reducing the damage to the original murals during the restoration process. Lastly, a diffusion model is employed for digital restoration of murals, improving the restoration performance by adjusting parameters to achieve natural repair of mural cracks. Experimental results show that comprehensive method based on the Unet network and diffusion model has significant advantages in the tasks of crack detection and digital restoration of murals, providing a novel and effective approach for the protection and restoration of ancient murals. In addition, this research has significant implications for the technological development in the field of mural restoration and cultural heritage preservation, contributing to the advancement and technological innovation in related fields.
期刊介绍:
The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys.
TETCI is an electronics only publication. TETCI publishes six issues per year.
Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.