research-article

Exemplar-Based Image Inpainting using Automatic Patch Optimization

Authors:

Yan ChaoAuthors Info & Claims

ICVIP '18: Proceedings of the 2018 2nd International Conference on Video and Image Processing

Pages 128 - 133

https://doi.org/10.1145/3301506.3301549

Published: 29 December 2018 Publication History

Abstract

Image inpainting has a wide range of applications in image processing. Exemplar-based technique can inpaint texture and structure regions simultaneously. However, the size of patch influences the result of inpainting. So far, there is no easy way to automatically determine the size of patch. Structure tensor can be used to determine priority, because it is able to determine the properties of a local area. In the paper, the size of patch is computed by structure tensor when adopting exemplar-based technique. To reduce blockiness, boundary constraints is added for searching for similar patches. The experiments show our proposed method can inpaint texture, flat, structure images. Adding boundary constraints can eliminate blockiness to a certain extent.

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Cited By

Mingxuan ZWei LHui LRuinan ZYiming D(2022)Anti-gravity stem-seeking restoration algorithm for maize seed root image phenotype detectionComputers and Electronics in Agriculture10.1016/j.compag.2022.107337202(107337)Online publication date: Nov-2022
https://doi.org/10.1016/j.compag.2022.107337
Sharma SRani R(2022)An Ensembled Intense Approach for Image InpaintingRecent Advances in Mechanical Engineering10.1007/978-981-19-2188-9_55(609-617)Online publication date: 9-Sep-2022
https://doi.org/10.1007/978-981-19-2188-9_55
Wang JZhou YSima HHuo ZMi A(2021)Image Inpainting Based on Structural Tensor Edge Intensity ModelInternational Journal of Automation and Computing10.1007/s11633-020-1256-x18:2(256-265)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1007/s11633-020-1256-x

Index Terms

Exemplar-Based Image Inpainting using Automatic Patch Optimization
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Image processing

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cover image ACM Other conferences

ICVIP '18: Proceedings of the 2018 2nd International Conference on Video and Image Processing

December 2018

252 pages

ISBN:9781450366137

DOI:10.1145/3301506

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Kyoto University: Kyoto University
TU: Tianjin University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 December 2018

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Key Projects of Natural Science Research in Anhui Colleges and Universities
Key Research and Development Program of Jiangsu Province
Key Fund Projects of Young Talents in Fuyang Normal University
National Natural Science Foundation of China
Postgraduate Research & Practice Innovation Program of Jiangsu Province

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ICVIP 2018

ICVIP 2018: 2018 the 2nd International Conference on Video and Image Processing

December 29 - 31, 2018

Hong Kong, Hong Kong

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Cited By

Mingxuan ZWei LHui LRuinan ZYiming D(2022)Anti-gravity stem-seeking restoration algorithm for maize seed root image phenotype detectionComputers and Electronics in Agriculture10.1016/j.compag.2022.107337202(107337)Online publication date: Nov-2022
https://doi.org/10.1016/j.compag.2022.107337
Sharma SRani R(2022)An Ensembled Intense Approach for Image InpaintingRecent Advances in Mechanical Engineering10.1007/978-981-19-2188-9_55(609-617)Online publication date: 9-Sep-2022
https://doi.org/10.1007/978-981-19-2188-9_55
Wang JZhou YSima HHuo ZMi A(2021)Image Inpainting Based on Structural Tensor Edge Intensity ModelInternational Journal of Automation and Computing10.1007/s11633-020-1256-x18:2(256-265)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1007/s11633-020-1256-x

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