TY - JOUR
T1 - Nonlocal total variation regularization with Shape Adaptive Patches for image denoising via Split Bregman method
AU - Ma, Shuli
AU - Du, Huiqian
AU - Mei, Wenbo
AU - Jia, Luliang
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2021/5/24
Y1 - 2021/5/24
N2 - 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.
AB - 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.
KW - Image denoising
KW - Nonlocal total variation
KW - Split Bregman algorithm
KW - aggregation
UR - http://www.scopus.com/inward/record.url?scp=85107407120&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1914/1/012008
DO - 10.1088/1742-6596/1914/1/012008
M3 - Conference article
AN - SCOPUS:85107407120
SN - 1742-6588
VL - 1914
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012008
T2 - 2021 International Conference on Electrical, Electronics and Computing Technology, EECT 2021
Y2 - 26 March 2021 through 28 March 2021
ER -