A New Method for Choosing the Regularization Parameter of ROF Total Variation Image Denoising

被引：1
|
作者：
Zhang, Lan [1 ]
Xu, Lei [2 ]
机构：
[1] Northwestern Polytech Univ, Dept Appl Math, Xian, Shaanxi, Peoples R China
[2] Xian Aeronaut Ploytech Inst, Dept Aeronaut Mat, Xian, Shaanxi, Peoples R China
来源：
2016 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS (IHMSC), VOL. 1 | 2016年
关键词：
Image denoising; Regularization parameter; Quantum particle swarm optimization;
D O I：
10.1109/IHMSC.2016.225
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The total variation regularization denoising is a widely used denoising method. But the denoising effect of this method depends on the choice of the regularization parameter. In this paper, estimate parameter of ROF total variation denoising based on quantum particle swarm optimization (QPSO) algorithm is proposed. At per iteration step, we also fit a model about the optimal parameter and standard deviation of Gaussian noise. The experimental results show that the proposed method can obtain favourable denoising result and has a good performance in PSNR.
引用
收藏
页码：373 / 377
页数：5
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