DocumentCode :
3777467
Title :
An ?1/2-BTV regularization algorithm for super-resolution
Author :
Weijian Liu; Zeqi Chen; Yunhua Chen; Ruohe Yao
Author_Institution :
School of Electronic and Information Engineering, South China University of Technology, No. 381, Wushan Road, Tianhe District, Guangzhou, China, 510640
Volume :
1
fYear :
2015
Firstpage :
1274
Lastpage :
1281
Abstract :
In this paper, we propose a novelregularization term for super-resolution by combining a bilateral total variation (BTV) regularizer and a sparsity prior model on the image. The term is composed of the weighted least squares minimization and the bilateral filter proposed by Elad, but adding an ?1/2 regularizer. It is referred to as ?1/2-BTV. The proposed algorithm serves to restore image details more precisely and eliminate image noise more effectively by introducing the sparsity of the ?1/2 regularizer into the traditional bilateral total variation (BTV) regularizer. Experiments were conducted on both simulated and real image sequences. The results show that the proposed algorithm generates high-resolution images of better quality, as defined by both de-noising and edge-preservation metrics, than other methods.
Keywords :
"Image edge detection","Image reconstruction","Histograms","Spatial resolution","Minimization","Adaptation models"
Publisher :
ieee
Conference_Titel :
Computer Science and Network Technology (ICCSNT), 2015 4th International Conference on
Type :
conf
DOI :
10.1109/ICCSNT.2015.7490963
Filename :
7490963
Link To Document :
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