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Image denoising using non-local fuzzy means
Rushi Lan; Yicong Zhou; Yuan Yan Tang; C. L. Philip Chen
2015-08-31
Conference NameIEEE China Summit & International Conference on Signal and Information Processing
Source Publication2015 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015 - Proceedings
Pages196-200
Conference DateJUL 12-15, 2015
Conference PlaceChengdu, PEOPLES R CHINA
CountryCHINA
PublisherIEEE
Abstract

Due to the fact that the dissimilarity between the centered and other patches within an image dynamically changes in each denoising iteration, this paper proposes a new non-local image denoising algorithm called the non-local fuzzy means (NLFM). It considers the weights as fuzzy variables and adaptively update their values by solving an energy minimization problem. A new exponential parameter is introduced to the weights, offering a nonlinear mapping to enhance the NLFM's denoising performance. Two strategies are proposed to solve the energy minimization problem in different parameter settings. Experiments demonstrate that the NLFM outperforms several existing non-local means algorithms in terms of visual quality and quantitative measures.

KeywordImage Denoising Non-local Fuzzy Means (Nlfm) Non-local Means (Nlm)
DOIhttp://doi.org/10.1109/ChinaSIP.2015.7230390
URLView the original
Indexed BySCI
Language英语
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000380436500041
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Cited Times [WOS]:8   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationDepartment of Computer and Information Science University of Macau, Macau 999078, China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Rushi Lan,Yicong Zhou,Yuan Yan Tang,et al. Image denoising using non-local fuzzy means[C]:IEEE,2015:196-200.
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