Group-based truncated l1-2 model for image inpainting

We propose a novel image inpainting model that can effectively estimate missing pixels in an observed image. The latent image is characterized by a group-based low-rank prior, which assumes that a group of vectorized similar image patches can be well approximated by a low-rank matrix. We enforce the...

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Bibliographic Details

Published in:	2017 IEEE International Conference on Image Processing (ICIP) pp. 2079 - 2083
Main Authors:	Ma, Tian-Hui, Lou, Yifei, Huang, Ting-Zhu, Zhao, Xi-Le
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.09.2017
Subjects:	Approximation algorithms Computational modeling Convergence Convex functions Difference of convex functions algorithm image inpainting l 1-2 minimization low-rank matrix approximation Mathematical model Measurement Minimization nonlocal self-similarity
ISSN:	2381-8549
Online Access:	Get full text
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