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Missing Elements Recovery Using Low-Rank Tensor Completion and Total Variation Minimization

  • Jinglin Zhang
  • Mengjie Qin
  • Cong Bai
  • Jianwei ZhengEmail author
Conference paper
  • 42 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1181)

Abstract

The Low-rank (LR) and total variation (TV) are two most popular regularizations for image processing problems and have sparked a tremendous number of researches, particularly for moving from scalar to vector, matrix or even high-order based functions. However, discretization schemes commonly used for TV regularization often ignore the difference of the intrinsic properties, which is not effective enough to exploit the local smoothness, let alone the problem of edge blurring. To address this issue, in this paper, we consider the color image as three-dimensional tensors, then measure the smoothness of these tensors by TV norm along the different dimensions. The three-order tensor is then recovered by Tucker decomposition factorization. Specifically, we propose integrating Shannon total variation (STV) into low-rank tensor completion (LRTC). Moreover, due to the suboptimality of nuclear norm, we propose a new nonconvex low-rank constraint for closer rank approximation, namely truncated \(\gamma \)-norm. We solve the cost function using the alternating direction method of multipliers (ADMM) method. Experiments on color image inpainting tasks demonstrate that the proposed method enhances the details of the recovered images.

Keywords

Tensor completion Low-rank Shannon total variation 

Notes

Acknowledgment

This research is funded by Natural Science Foundation of China under Grant Nos. 61702275, 61976192, 61602413, 41775008, and by Zhejiang Provincial Natural Science Foundation of China under Grant Nos. LY18F020032 and LY19F030016.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Jinglin Zhang
    • 1
  • Mengjie Qin
    • 2
  • Cong Bai
    • 2
  • Jianwei Zheng
    • 2
    Email author
  1. 1.School of Atmospheric ScienceNanjing University of Information ScienceNanjingChina
  2. 2.College of Computer ScienceZhejiang University of TechnologyHangzhouChina

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