Hierarchical Prior Regularized Matrix Factorization for Image Completion
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The recent low-rank prior based models solve the tensor completion problem efficiently. However, these models fail to exploit the local patterns of tensors, which compromises the performance of tensor completion. In this paper, we propose a novel hierarchical prior regularized matrix factorization model for tensor completion. This model hierarchically incorporates the low-rank prior, total variation prior, and sparse coding prior into a matrix factorization, simultaneously characterizing both the global low-rank property and the local smoothness of tensors. For solving the proposed model, we use the alternating direction method of multipliers to establish our algorithm. Besides, the complexity and convergence are investigated to further validate the algorithm effectiveness. The proposed scheme is then evaluated through various data sets. Experiment results verify that, the proposed method outperforms several state-of-the-art approaches.
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