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Unbiased distance based non-local fuzzy means
Li X.2; Zhou Y.1; Zhang J.2; Wang L.2
2018-09-10
Conference NameIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Source PublicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2018-April
Pages1423-1427
Conference DateAPR 15-20, 2018
Conference PlaceCalgary, CANADA
Abstract

This paper first introduces several unbiased distances to measure the similarity between image pixels and between image patches. Based on these unbiased distances, we propose a new non-local means denoising method, named Unbiased Distance based Non-local Fuzzy Means (UDNLFM). UDNLFM considers the weight as a fuzzy variable and updates its value in each denoising iteration. Experiments and comparisons demonstrate that UDNLFM outperforms state-of-the-art non-local means methods for image denoising.

KeywordImage Denoising Non-local Means Nonlocal Fuzzy Means Unbiased Distance
DOIhttp://doi.org/10.1109/ICASSP.2018.8462573
URLView the original
Indexed BySCI
Language英语
WOS Research AreaAcoustics ; Engineering
WOS SubjectAcoustics ; Engineering, Electrical & Electronic
WOS IDWOS:000446384601122
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Universidade de Macau
2.Hunan University
Recommended Citation
GB/T 7714
Li X.,Zhou Y.,Zhang J.,et al. Unbiased distance based non-local fuzzy means[C],2018:1423-1427.
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