UM
Tensor Decomposition for Poisson Image Denoising
Wei,Huiqin; Chen,Long; Xu,Lili
2019-11-01
Source Publication2019 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2019
Pages158-162
AbstractIn 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
DOI10.1109/iFUZZY46984.2019.9066222
URLView the original
Language英语
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Document TypeConference paper
CollectionUniversity of Macau
Corresponding AuthorChen,Long
AffiliationUniversity of Macau Avenida da Universidade,Department of Computer and Information Science,Taipa,Avenida da Universidade,Macao
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.
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