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Tensor Decomposition for Poisson Image Denoising
Wei,Huiqin; Chen,Long; Xu,Lili
Conference NameInternational Conference on Fuzzy Theory and Its Applications (iFUZZY)
Source Publication2019 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2019
Conference DateNOV 07-10, 2019
Conference PlaceNew Taipei City, TAIWAN

In this paper, we provide a new iterative algorithm via the weighted low-rank tensor model for Poisson image denoising. The noise variance of the scaled Poisson data is transformed at each iteration through the Anscombe transformation obtaining a signal where the noise is as additive white Gaussian noise. Then this Gaussian noise is removed using the weighted low-rank tensor filter with the local and nonlocal geometry information because of natural images containing patches with high similarity. Finally, an exact unbiased inverse is constructed to estimate the signal of interest. Experimental results demonstrate the superior performance of our proposed method compared with other Poisson denoising algorithms on the ten test images.

KeywordAnscombe Transformation Image Denoising Low-rank Tensor Poisson Noise
URLView the original
Indexed ByCPCI-S
WOS Research AreaComputer Science
WOS IDWOS:000569809300014
Scopus ID2-s2.0-85084171882
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
Corresponding AuthorChen,Long
AffiliationUniversity of Macau
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Wei,Huiqin,Chen,Long,Xu,Lili. Tensor Decomposition for Poisson Image Denoising[C],2019:158-162.
APA Wei,Huiqin,Chen,Long,&Xu,Lili.(2019).Tensor Decomposition for Poisson Image Denoising.2019 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2019,158-162.
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