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Low rank tensor completion for multiway visual data
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DOI：10.1016/j.sigpro.2018.09.039.png)
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Abstract
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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.
Keywords:
Low-rank tensor approximation
Color image and video completion
CANDECOMP/PARAFAC Decomposition
Tucker decomposition
Tensor singular value decomposition
Tensor network
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