系统仿真学报 ›› 2022, Vol. 34 ›› Issue (7): 1524-1531.doi: 10.16182/j.issn1004731x.joss.21-0034

• 仿真建模理论与方法 • 上一篇    下一篇

强化结构的数字壁画病害修复算法研究

张子迎(), 周华()   

  1. 北京联合大学 应用文理学院,北京 100191
  • 收稿日期:2021-01-13 修回日期:2021-03-29 出版日期:2022-07-30 发布日期:2022-07-20
  • 通讯作者: 周华 E-mail:ziying@buu.edu.cn;zhouhua@buu.edu.cn
  • 作者简介:张子迎(1987-),女,博士,讲师,研究法方向为数字化图像处理,文化遗产数字化。E-mail:ziying@buu.edu.cn
  • 基金资助:
    北京市教委科技计划一般项目(KM201911417015);北京市属高校高水平教师队伍建设支持计划青年拔尖人才培育计划(CIT&TCD201904074);北京联合大学人才强校优选百优计划(BPHR2019DS02)

Research on Inpainting Algorithm of Digital Murals Based on Enhanced Structural Information

Ziying Zhang(), Hua Zhou()   

  1. College of Applied Arts and Science, Beijing Union University, Beijing 100191, China
  • Received:2021-01-13 Revised:2021-03-29 Online:2022-07-30 Published:2022-07-20
  • Contact: Hua Zhou E-mail:ziying@buu.edu.cn;zhouhua@buu.edu.cn

摘要:

针对北京地区法海寺壁画块状缺失,且缺失区域结构信息丰富的特点,提出了一种强化结构的数字图像修复算法,解决了Criminisi算法修复时对图像结构信息考虑不足的问题。首先在计算填充块的优先权函数时,将线性卷积的曲率计算融入数据项中,同时增加结构信息的权重,实现了优先修复结构信息丰富区域的目的;其次在搜索匹配块的相似度计算中引入区域协方差方法,确保修复后图像结构的一致性,减少了匹配错误率。实验结果表明:该算法较好地解决了结构性较强的壁画块状信息缺失填充错位问题。

关键词: 图像修复, Criminisi算法, 优先权函数, 曲率, 区域协方差

Abstract:

According to the fact that the murals of Fahai Temple in Beijing are missing in blocks and the missing area is structure information, a structure-enhancing digital image restoration algorithm is proposed to solve the problem of insufficient consideration of image structure information in Criminisi algorithm. When calculating the priority function of the filling block, the curvature calculation of the linear convolution is integrated into the data item, and the weight of the structure information is increased to achieve the goal of repairing the structure information-rich region in priority; the regional covariance method is introduced in the similarity calculation of the search matching block to ensure the consistency of the repaired image structure and reduce the matching error rate. Experimental results show that the algorithm can solve the problem on mural with strong structure.

Key words: image inpainting, Crimanisi algorithm, priority function, curvature, region covariance

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