Modified total variation regularization using fuzzy complement for image denoising

Ahmed Ben Said; Sebti Foufou

doi:10.1109/IVCNZ.2015.7761561

Modified total variation regularization using fuzzy complement for image denoising

Ahmed Ben Said, Sebti Foufou

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper, we propose a denoising algorithm based on the Total Variation (TV) model. Specifically, we associate to the regularization term of the Rodin-Osher-Fatimi (ROF) functional a small weight whenever denoising is performed in edge and texture regions, which means less regularization and more details preservation. On the other hand, a large weight is associated if the region being filtered is smooth which means noise will be well suppressed. The weight computation is inspired from the fuzzy edge complement. Experiments on well-known images and comparison with state of the art denoising algorithms demonstrate that the proposed method not only presents good denoising performance but also is able to preserve edge information.

Original language	English (US)
Title of host publication	2015 International Conference on Image and Vision Computing New Zealand, IVCNZ 2015
Publisher	IEEE Computer Society
ISBN (Electronic)	9781509003570
DOIs	https://doi.org/10.1109/IVCNZ.2015.7761561
State	Published - Nov 28 2016
Event	2015 International Conference on Image and Vision Computing New Zealand, IVCNZ 2015 - Auckland, New Zealand Duration: Nov 23 2015 → Nov 24 2015

Publication series

Name	International Conference Image and Vision Computing New Zealand
Volume	2016-November
ISSN (Print)	2151-2191
ISSN (Electronic)	2151-2205

Other

Other	2015 International Conference on Image and Vision Computing New Zealand, IVCNZ 2015
Country/Territory	New Zealand
City	Auckland
Period	11/23/15 → 11/24/15

Keywords

denoising
edge detector
fuzzy complement
total variation

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering

Access to Document

10.1109/IVCNZ.2015.7761561

Cite this

Said, A. B., & Foufou, S. (2016). Modified total variation regularization using fuzzy complement for image denoising. In 2015 International Conference on Image and Vision Computing New Zealand, IVCNZ 2015 Article 7761561 (International Conference Image and Vision Computing New Zealand; Vol. 2016-November). IEEE Computer Society. https://doi.org/10.1109/IVCNZ.2015.7761561

Modified total variation regularization using fuzzy complement for image denoising. / Said, Ahmed Ben; Foufou, Sebti.
2015 International Conference on Image and Vision Computing New Zealand, IVCNZ 2015. IEEE Computer Society, 2016. 7761561 (International Conference Image and Vision Computing New Zealand; Vol. 2016-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Said, AB & Foufou, S 2016, Modified total variation regularization using fuzzy complement for image denoising. in 2015 International Conference on Image and Vision Computing New Zealand, IVCNZ 2015., 7761561, International Conference Image and Vision Computing New Zealand, vol. 2016-November, IEEE Computer Society, 2015 International Conference on Image and Vision Computing New Zealand, IVCNZ 2015, Auckland, New Zealand, 11/23/15. https://doi.org/10.1109/IVCNZ.2015.7761561

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AB - In this paper, we propose a denoising algorithm based on the Total Variation (TV) model. Specifically, we associate to the regularization term of the Rodin-Osher-Fatimi (ROF) functional a small weight whenever denoising is performed in edge and texture regions, which means less regularization and more details preservation. On the other hand, a large weight is associated if the region being filtered is smooth which means noise will be well suppressed. The weight computation is inspired from the fuzzy edge complement. Experiments on well-known images and comparison with state of the art denoising algorithms demonstrate that the proposed method not only presents good denoising performance but also is able to preserve edge information.

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Modified total variation regularization using fuzzy complement for image denoising

Abstract

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Keywords

ASJC Scopus subject areas

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