中文摘要：

首先从局部坐标角度分析整体变分（TV）模型与p-Laplace算子的物理意义,从本质上说明p-Laplace算子的扩散性能优于TV模型,进而提出一种基于p-Laplace算子的图像修补算法.该算法利用p-Laplace算子的非线性各向异性扩散的性能来填充受损区域.与TV修补算法相比,文中算法能快速收敛,并达到更好的修补效果,其综合性能优于TV修补算法.

英文摘要：

The physical characteristics of total variation （TV） model and p-Laplace operator in local coordinates are analyzed, which shows that the diffusion performance of p-Laplace operator is essentially superior to that of TV model. Then, an image inpainting algorithm based on p-Laplace operator is proposed. This algorithm makes full use of the nonlinear anisotropic diffusion of p-Laplace operator to inpaint damaged images. Experimental results show that the proposed algorithm has better general performance in convergence speed and inpainting quality.