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Authors: Thibaud Ehret ; Pablo Arias and Jean-Michel Morel

Affiliation: CMLA and ENS Cachan, France

Keyword(s): Video Denoising, Patch-based Methods, Patch Search, Nearest Neighbors Search.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image Enhancement and Restoration ; Image Formation and Preprocessing

Abstract: With the increasing popularity of mobile imaging devices and the emergence of HdR video surveillance, the need for fast and accurate denoising algorithms has also increased. Patch-based methods, which are currently state-of-the-art in image and video denoising, search for similar patches in the signal. This search is generally performed locally around each target patch for obvious complexity reasons. We propose here a new and efficient approximate patch search algorithm. It permits for the first time to evaluate the impact of a global search on the video denoising performance. A global search is particularly justified in video denoising, where a strong temporal redundancy is often available. We first verify that the patches found by our new approximate search are far more concentrated than those obtained by exact local search, and are obtained in comparable time. To demonstrate the potential of the global search in video denoising, we take two patch-based image denoising algorithms a nd apply them to video. While with a classical local search their performance is poor, with the proposed global search they even improve the latest state-of-the-art video denoising methods. (More)

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Paper citation in several formats:
Ehret, T.; Arias, P. and Morel, J. (2017). Global Patch Search Boosts Video Denoising. In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 4: VISAPP; ISBN 978-989-758-225-7; ISSN 2184-4321, SciTePress, pages 124-134. DOI: 10.5220/0006175601240134

@conference{visapp17,
author={Thibaud Ehret. and Pablo Arias. and Jean{-}Michel Morel.},
title={Global Patch Search Boosts Video Denoising},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 4: VISAPP},
year={2017},
pages={124-134},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006175601240134},
isbn={978-989-758-225-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 4: VISAPP
TI - Global Patch Search Boosts Video Denoising
SN - 978-989-758-225-7
IS - 2184-4321
AU - Ehret, T.
AU - Arias, P.
AU - Morel, J.
PY - 2017
SP - 124
EP - 134
DO - 10.5220/0006175601240134
PB - SciTePress