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Spatially Regularized Low Rank Tensor Optimization for Visual Data Completion

Gao, Jianchao, Shi, Hong and Wang, WenwuW (2018) Spatially Regularized Low Rank Tensor Optimization for Visual Data Completion In: 2018 25th IEEE International Conference on Image Processing (ICIP), 07-10 Oct 2018, Athens, Greece.

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Abstract

Low-rank tensor completion is a recent method for estimating the values of the missing elements in tensor data by minimizing the tensor rank. However, with only the low rank prior, the local piecewise smooth structure that is important for visual data is not used effectively. To address this problem, we define a new spatial regularization S-norm for tensor completion in order to exploit the local spatial smoothness structure of visual data. More specifically, we introduce the S-norm to the tensor completion model based on a non-convex LogDet function. The S-norm helps to drive the neighborhood elements towards similar values. We utilize the Alternating Direction Method of Multiplier (ADMM) to optimize the proposed model. Experimental results in visual data demonstrate that our method outperforms the state-of-the-art tensor completion models.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Gao, Jianchao
Shi, Hong
Wang, WenwuWW.Wang@surrey.ac.uk
Date : 6 September 2018
DOI : 10.1109/ICIP.2018.8451382
Copyright Disclaimer : 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Uncontrolled Keywords : Tensor completion; S-norm; LogDet function; Low rank; Visual data processing
Related URLs :
Depositing User : Clive Harris
Date Deposited : 19 Sep 2018 07:16
Last Modified : 07 Oct 2018 02:08
URI: http://epubs.surrey.ac.uk/id/eprint/849346

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