6533b858fe1ef96bd12b5b44
RESEARCH PRODUCT
Modified total variation regularization using fuzzy complement for image denoising
Sebti FoufouAhmed Ben Saidsubject
fuzzy complementbusiness.industryNoise reductionPattern recognitionTotal variation denoisingNon-local meansRegularization (mathematics)Fuzzy logicElectronic mailtotal variationComputer Science::Computer Vision and Pattern RecognitiondenoisingComputer visionVideo denoisingArtificial intelligenceNoise (video)edge detectorbusinessMathematicsdescription
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. Scopus
year | journal | country | edition | language |
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2015-11-01 | 2015 International Conference on Image and Vision Computing New Zealand (IVCNZ) |