Fuzzy density weight-based support vector regression for image denoising

被引：9

作者：

Zhang, Yun ^{[1
]}

Xu, Shuqiong ^{[1
]}

Chen, Kairui ^{[1
]}

Liu, Zhi ^{[1
]}

Chen, C. L. Philip ^{[2
]}

机构：

[1] Guangdong Univ Technol, Fac Automat, Guangzhou, Guangdong, Peoples R China

[2] Univ Macau, Fac Sci & Technol, Macau, Peoples R China

来源：

INFORMATION SCIENCES | 2016年 / 339卷

基金：

中国国家自然科学基金; 国家教育部博士点专项基金资助;

关键词：

Fuzzy density weight; Least squares support vector regression; Image denoising; WAVELET DOMAIN; MACHINE; MINIMIZATION; TRANSFORM; REMOVAL; FIELDS; FILTER; NOISE; MODEL;

D O I：

10.1016/j.ins.2016.01.007

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Support vector machine (SVM) is a popular machine learning technique and its variant least squares support vector regression (LS-SVR) is effective for image denoising. However, conventional LS-SVR does not fully consider the sampling distribution of noisy images, which may degrade the performance of the algorithm. In this paper, we propose a new fuzzy density weight SVR (FDW-SVR) denoising algorithm, which assigns fuzzy priority to each sample according to its density weight. FDW is designed to estimate the joint probability density function via the fuzzy theory based on the pixel density and neighborhood density. Extensive experimental results show that FDW-SVR is superior to those state-of-the-art denoising techniques in light of both subjective and objective evaluations. (C) 2016 Elsevier Inc. All rights reserved.

引用

页码：175 / 188

页数：14

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