Simultaneous Visual Data Completion and Denoising Based on Tensor Rank and Total Variation Minimization and Its Primal-Dual Splitting Algorithm
Simultaneous Visual Data Completion and Denoising Based on Tensor Rank and Total Variation Minimization and Its Primal-Dual Splitting Algorithm
复制标题
DOI：
10.1109/cvpr.2017.409
复制
发表时间：
2017-07
期刊：
影响因子：
--
通讯作者：
Tatsuya Yokota;H. Hontani
中科院分区：
文献类型：
--
作者：
Tatsuya Yokota;H. Hontani
张量补全因其具有良好的能力和通用性而备受关注。然而，直接解决由噪声不等式约束组成的优化问题的研究很少。本文提出了一种新的张量补全去噪模型，该模型包含张量总变分和张量核范数最小化，具有一定范围的值和噪声不等式。此外，我们开发了基于原始对偶分裂方法的求解算法，与基于张量分解的非凸优化相比，该算法的计算效率更高。最后，大量的实验证明了该方法在彩色图像、电影和3d体积数据等视觉数据检索中的优势。
Tensor completion has attracted attention because of its promising ability and generality. However, there are few studies on noisy scenarios which directly solve an optimization problem consisting of a noise inequality constraint. In this paper, we propose a new tensor completion and denoising model including tensor total variation and tensor nuclear norm minimization with a range of values and noise inequalities. Furthermore, we developed its solution algorithm based on a primal-dual splitting method, which is computationally efficient as compared to tensor decomposition based non-convex optimization. Lastly, extensive experiments demonstrated the advantages of the proposed method for visual data retrieval such as for color images, movies, and 3D-volumetric data.