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图学学报 ›› 2021, Vol. 42 ›› Issue (4): 590-598.DOI: 10.11996/JG.j.2095-302X.2021040590

• 图像处理与计算机视觉 • 上一篇    下一篇

基于边缘重建的多尺度壁画修复方法

  

  1. 云南大学信息学院,云南 昆明 650500
  • 出版日期:2021-08-31 发布日期:2021-08-05
  • 基金资助:
    国家自然科学基金项目(61540062,61761046);云南省“万人计划”云岭学者专项;云南省科技厅-云南大学“双一流”建设联合基金项目
    (2019FY003012)

Multi-scale mural restoration method based on edge reconstruction

  1. School of Information Science and Engineering, Yunnan University, Kunming Yunnan 650500, China
  • Online:2021-08-31 Published:2021-08-05
  • Supported by:
    National Natural Science Foundation of China (61540062, 61761046); Yunnan Province “Ten Thousand Talents Program” Yunling
    Scholars Special; Yunnan Provincial Science and Technology Department-Yunnan University “Double-First Class” Construction
    Joint Fund Project (2019FY003012)

摘要: 中国古壁画历经千百年历史,不可避免地出现了不同程度的破损,其修复研究具有重要的历史
价值和文化价值。传统的手工修复直接作用于壁画,操作不当会造成保护性破坏,所以采用数字虚拟修复,提
出了基于边缘重建的多尺度壁画修复方法。由于现存壁画数量较少,通过收集与整理我国不同地域年代的壁画
获得了实验数据。对自由裁剪后的壁画进行滤波平滑图像细节并保留其边缘,为破损区域的图像分割提供良好
的初始化条件,经过图像聚类自动计算待修复掩膜;修复过程以边缘重建为基础,对破损壁画在多尺度空间中
提取纹理特征,充分挖掘图像已知的可用信息进行其缺失内容的填补。实验结果表明,该方法不受壁画种类和
破损程度的限制,整个重建过程更加通用和有效,能够恢复得到具有结构完整和纹理清晰的图像,并取得较好
的修复效果。

关键词: 数字虚拟修复, 图像滤波, 分割模型, 图像聚类, 边缘重建, 多尺度修复

Abstract: Ancient Chinese murals, with a history of thousands of years, inevitably have been damaged to varying
degrees, so the relevant restoration research is of great historical and cultural value. In the process of the traditional
manual restoration directly affecting the mural itself, improper operations will cause protective damages. Therefore,
digital virtual restoration was adopted, and a multi-scale mural restoration method based on edge reconstruction was
proposed. Due to the scarcity of existing murals, the experimental data was obtained by collecting and sorting out
different regions of Chinese murals. The freely cropped murals were filtered to smooth the image details and retain
their edges, which provided good initialization conditions for the image segmentation of damaged areas, then
automatic calculations were conducted on the mask to be repaired after image clustering. The repair process was based
on edge reconstruction, texture features of damaged murals were extracted in a multi-scale space, and the available
known information of the image was fully excavated to fill in its missing content. The experimental results show that the method is not limited by the type and degree of damages to the murals, the entire reconstruction process is more
versatile and effective, and it can recover images with complete structure and clear texture, and achieve better repair
results.

Key words: digital virtual repair, image filtering, segmentation model, image clustering, edge reconstruction;
multi-scale repair

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