论文标题
通过加权HOSVD初始化的总变化完成张量完成
Tensor Completion through Total Variationwith Initialization from Weighted HOSVD
论文作者
论文摘要
在我们的论文中，当采样模式是确定性的时，我们研究了张量的完成问题。我们首先提出了一种简单但有效的加权HOSVD算法，用于从嘈杂的观察中恢复。然后，我们将加权HOSVD结果用作总变化的初始化。我们从理论和数值角度证明了加权HOSVD算法的准确性。在数值模拟零件中，我们还表明，通过使用建议的初始化，总变化算法可以有效地填充图像和视频的缺失数据。
In our paper, we have studied the tensor completion problem when the sampling pattern is deterministic. We first propose a simple but efficient weighted HOSVD algorithm for recovery from noisy observations. Then we use the weighted HOSVD result as an initialization for the total variation. We have proved the accuracy of the weighted HOSVD algorithm from theoretical and numerical perspectives. In the numerical simulation parts, we also showed that by using the proposed initialization, the total variation algorithm can efficiently fill the missing data for images and videos.
