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Low-Rank Tensor Completion with Total-Variation-Regularized Transformed Tensor Schatten-p Norm for Video Inpainting by Jiahui Liu; Jialue Tian is a scholarly article available to read on EtoBox.

What is Low-Rank Tensor Completion with Total-Variation-Regularized Transformed Tensor Schatten-p Norm for Video Inpainting about?

Due to the existence of missing entries in real-world tensor data, low-rank tensor completion (LRTC) problem has received increasing attention. In this paper, we propose a new transformed tensor Schatten-p norm to replace the rank norm and develop a transformed multi-tensor-Schatten-p norm surrogate theorem to convert the non-convex transformed tensor Schatten-p norm with 0<p<1 into the sum of multiple convex functions. However, tensor completion constrained by low-rank prior alone cannot protect local smoothness along the spatial and tubal dimensions. To address this drawback, we combine anisotropic total variation (TV) regularization with non-convex transformed tensor Schatten-p norm with 0<p<1 for LRTC. The combination of global low-rank prior and local TV prior is beneficial to improving the final completion effect. Our experimental results on grey-scale video inpainting demonstrate that our proposed method outperforms other existing state-of-the-art methods. CCS CONCEPTS • Computer vision → Image processing; Mathematical optimization.

Author

Jiahui Liu; Jialue Tian

Publisher

ACM

Published

2022

Language

EN