DocumentCode :
264925
Title :
Iterative Total Variation Image Deblurring with Varying Regularized Parameter
Author :
Binbin Hao ; Jianguang Zhu ; Yan Hao
Author_Institution :
Coll. of Sci., China Univ. of Pet., Qingdao, China
Volume :
1
fYear :
2014
fDate :
26-27 Aug. 2014
Firstpage :
249
Lastpage :
252
Abstract :
Total variation based model is one of the most effective method for image restoration. In this paper, we consider the total variation (TV) based regularization method and evaluate the regularization parameter for the TV based iterative forward-backward splitting (IFBS) approach. Different parameters with different iterations are obtained. The proposed adaptive iterative forward-backward splitting method does not need to know the initial value of the regularization parameter and does not require any information about the perturbation process. Experimental results demonstrate that the adaptive parameter method is efficient and provide competitive performance.
Keywords :
image restoration; iterative methods; IFBS approach; TV based iterative forward-backward splitting; TV based regularization method; adaptive iterative forward-backward splitting method; adaptive parameter method; image restoration; iterations; iterative total variation image deblurring; perturbation process; regularization parameter; total variation based model; Image Deblurring; Regularization Parameter; Splitting methods; Total Variation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2014 Sixth International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4799-4956-4
Type :
conf
DOI :
10.1109/IHMSC.2014.68
Filename :
6917351
Link To Document :
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