Open AccessNonlocal total variation regularization with Shape Adaptive Patches for image denoising via Split Bregman method
Author(s) -
Shuli Ma,
Heng Du,
Wenbo Mei,
Luliang Jia
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1914/1/012008
Subject(s) - noise reduction , total variation denoising , regularization (linguistics) , estimator , non local means , image (mathematics) , mathematics , video denoising , algorithm , image denoising , noise (video) , artificial intelligence , computer science , pattern recognition (psychology) , mathematical optimization , statistics , video tracking , object (grammar) , multiview video coding
Non-Local or patch-based approaches are used in most of the state-of-the-art methods for image denoising. Denoising with regular square patches may cause noise halos around edges, particularly high contrasted edges. In order to overcome this drawback, this work presents an extension of the Non-Local TV framework that effectively exploits the potential local geometric features of the image by replacing the simple square patches with different oriented shapes. We first solve the denoised models of ROF-NLTV with different oriented patch shapes by Split Bregman algorithm. Then, use exponentially weighted aggregation method based on Stein’s unbiased risk estimate to combine the estimators obtained in the first step. The numerical results show our method outperforms some previous ones. Moreover, common noise halos around edges usually observed by denoising with Non-Local TV method are reduced thanks our approach.