图像修复中截断P范数正则化的矩阵填充算法（英文）

Matrix Completion via Truncated Schatten p-norm Regularization in Image Inpainting

本文将截断核范数与Schantten-p范数结合起来,提出了一种更加灵活的矩阵填充算法,以便更好地利用图像修复中的低秩特性。我们进一步应用乘法器的交替方向法,提出了一种有效的迭代方案来解决优化问题。实验结果表明,我们提出的算法在真实的可视化数据集上展现的性能优于传统的矩阵填充算法。

In this paper,we develop a more flexible matrix completion algorithm toward better exploiting low rank property in image inpainting by jointing truncated nuclear norm and Schatten p-norm.We further apply the alternating direction method of multipliers to develop an efficient iterative scheme to solve the optimization problem.Experimental results demonstrate that our proposed algorithm can encouragingly outperform the state of the art matrix completion algorithms on real visual datasets.