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Graph-Regularized NMF with Prior Knowledge for Image Inpainting

  • Li LiuEmail author
  • Fei Shang
  • Siqi Chen
  • Yumei Wang
  • Xue Wang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 834)

Abstract

The image inpainting problem can be converted to the matrix completion. A classical matrix completion method is based on matrix factorization. The product of two low-rank matrices fills in the missing regions. In this paper, we propose a novel matrix factorization framework to recover the images. Before decomposing the original matrix, approximation matrix as the prior knowledge is constructed to estimate the values of missing pixels. The estimation of the missing pixels can be obtained through resampling from the surface fitting the 3D projection points of the available pixels. To keep the latent geometrical structure between adjacent pixels, we modify the graph-regularized which allows the edge weights negative to decompose the approximation matrix. Experimental results of image inpainting demonstrate the effectiveness of the proposed method compared with the representative methods in quantities.

Keywords

Graph-regularized NMF Image inpainting Hole filling Approximation matrix 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (61702310 and 61772322).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Li Liu
    • 1
    Email author
  • Fei Shang
    • 1
  • Siqi Chen
    • 1
  • Yumei Wang
    • 1
  • Xue Wang
    • 1
  1. 1.School of Information Science and EngineeringShandong Normal UniveristyJinanChina

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