Low-rank tensor completion via combined non-local self-similarity and low-rank regularization

Global low-rank methods have achieved great successes in tensor completion. However, these methods neglected the abundant non-local self-similarities, which exist in a wide range of multi-dimensional imaging data. To integrate the global and non-local property of the underlying tensor, we propose a novel low-rank tensor completion model via combined non-local self-similarity and low-rank regularization, which is named as NLS-LR. We adopt the parallel low-rank matrix factorization to guarantee the global low-rankness while plugging in non-local based denoisers to promote the non-local self-similarity instead of tailoring regularizers. To tackle the proposed model, we develop an efficient block successive upper-bound minimization (BSUM) based algorithm. Numerical experiment results demonstrate that the proposed method outperforms many state-of-the-art tensor completion methods in terms of quality metrics and visual effects. (C) 2019 Elsevier B.V. All rights reserved.