引用本文: 李清泉, 王欢, 邹勤. 一种基于稀疏表示模型的壁画修复算法[J]. 武汉大学学报 ( 信息科学版), 2018, 43(12): 1847-1853.
LI Qingquan, WANG Huan, ZOU Qin. A Murals Inpainting Algorithm Based on Sparse Representation Model[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 1847-1853.
 Citation: LI Qingquan, WANG Huan, ZOU Qin. A Murals Inpainting Algorithm Based on Sparse Representation Model[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 1847-1853.

## A Murals Inpainting Algorithm Based on Sparse Representation Model

• 摘要: 古代壁画受保存时间、保存环境、保护技术等限制，无法避免地承受着如褪色、脱落甚至大面积起甲等损害。传统人力手工修复技术存在操作不可逆的问题，因此数字图像修复技术被广泛用于虚拟修复中。提出了一种线描图指引下基于稀疏表示模型的壁画修复算法。首先，利用人机交互的方式将破损壁画中缺失的结构信息根据对应的线描图补全；之后，通过对待修复块的分类，定义了一种先纹理后结构的全新修复策略；接着，运用结构复杂度排序和全局随机抽取策略分别提高修复的准确性和效率；最后，利用稀疏表示模型求解候选块的线性组合去填充待修复区域。实验结果表明，所提算法对敦煌壁画破损图像修复具有较好效果。

Abstract: Ancient mural paintings are often suffered from damages such as color degradation, pigment peeling and even large-area shedding. Image inpainting techniques are widely used to virtually repair these damages. Firstly, we utilize the human-computer interaction techniques to complete missing structure information in the damaged areas according to the line drawings. Secondly, according to the classification of patches into textures and structures, a novel patch selection scheme from texture patch to structure patch is designed. Then, the order of patch structure complexity and the global randomly-selected strategy increase the inpainting accuracy and the efficiency. Finally, the sparse linear combination of candidate patches is constructed to sharply estimate the selected patch to be filled in a framework of sparse representation. Experimental results show the superior performance of the proposed method on damaged Dunhuang mural images.

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