Journal
SIGNAL PROCESSING
Volume 155, Issue -, Pages 301-316Publisher
ELSEVIER
DOI: 10.1016/j.sigpro.2018.09.039
Keywords
Low-rank tensor approximation; Color image and video completion; CANDECOMP/PARAFAC Decomposition; Tucker decomposition; Tensor singular value decomposition; Tensor network
Categories
Funding
- National Natural Science Foundation of China (NSFC) [61602091, 61571102]
- Fundamental Research Funds for the Central Universities [ZYGX2016J199, ZYGX2014Z003]
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] Tensor completion recovers missing entries of multiway data. The missing of entries could often be caused during the data acquisition and transformation. In this paper, we provide an overview of recent development in low-rank tensor completion for estimating the missing components of visual data, e.g., color images and videos. First, we categorize these methods into two groups based on the different optimization models. One optimizes factors of tensor decompositions with predefined tensor rank. The other iteratively updates the estimated tensor via minimizing the tensor rank. Besides, we summarize the corresponding algorithms to solve those optimization problems in details. Numerical experiments are given to demonstrate the performance comparison when different methods are applied to color image and video processing. (C) 2018 Elsevier B.V. All rights reserved.
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