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Projective Representation Learning for Discriminative Face Recognition Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Exemplar-Based Pixel by Pixel Inpainting Based on Patch Shift

Authors : Zhenping Qiang, Libo He, Dan Xu

Published in: Computer Vision

Publisher: Springer Singapore

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Abstract

This paper presents a novel exemplar-based image inpainting method. It performs patch-shift based information statistics and incorporates the result into the data filling process. The central idea to ensure the confidence pixel value to fill is based on patch shift that captures the statistic information from the expanded filling patches’ most similar patches. Our approach outperforms the original exemplar-based inpainting in a large number of examples on removing large occluding objects and thin scratches in real and synthetic images, at the cost of some additional computation. It inherits well-known advantages of the exemplar-based inpainting method, such as the capability of maintaining the integrity and consistency of image structure information, and the adaptability to many application scenarios. Experiments on synthetic and natural images show the advantages of our approach, especially, in the big target removing task our method yields generally better results.

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Metadata
Title
Exemplar-Based Pixel by Pixel Inpainting Based on Patch Shift
Authors
Zhenping Qiang
Libo He
Dan Xu
Copyright Year
2017
Publisher
Springer Singapore
Book
Computer Vision

Print ISBN: 978-981-10-7301-4

Electronic ISBN: 978-981-10-7302-1

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-7302-1_31

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