Least squares support vector regression and interval type-2 fuzzy density weight for scene denoising

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• 作者：Shuqiong Xu ; Zhi Liu ; Yun Zhang
• 关键词：Interval type ; 2 fuzzy density weights ; Fuzzy logic system ; Support vector regression
• 刊名：Soft Computing - A Fusion of Foundations, Methodologies and Applications
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：20
• 期：4
• 页码：1459-1470
• 全文大小：1,693 KB
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• 作者单位：Shuqiong Xu (1) (2)
  Zhi Liu (2)
  Yun Zhang (2)
  
  1. Department of Electronic Engineering, Dongguan Polytechnic, Dongguan, Guangdong, China
  2. School of Automation, Guangdong University of Technology, Guangzhou, Guangdong, China
  
• 刊物类别：Engineering
• 刊物主题：Numerical and Computational Methods in Engineering
  Theory of Computation
  Computing Methodologies
  Mathematical Logic and Foundations
  Control Engineering
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1433-7479

文摘

Support vector machines are the popular machine learning techniques. Its variant least squares support vector regression (LS-SVR) is effective for image denoising. However, the fitting of the samples contaminated by noises in the training phase will result in the fact that LS-SVR cannot work well when noise level is too far from it or noise density is high. Type-2 fuzzy sets and systems have been shown to be a more promising method to manifest the uncertainties. Various noises would be taken as uncertainties in scene images. By integrating the design of learning weights with type-2 fuzzy sets, a systematic design methodology of interval type-2 fuzzy density weighted support vector regression (IT2FDW-SVR) model for scene denoising is presented to address the problem of sample uncertainty in scene images. A novel strategy is used to design the learning weights, which is similar to the selection of human experience. To handle the uncertainty of sample density, interval type-2 fuzzy logic system (IT2FLS) is employed to deduce the fuzzy learning weights (IT2FDW) in the IT2FDW-SVR, which is an extension of the previously weighted SVR. Extensive experimental results demonstrate that the proposed method can achieve better performances in terms of both objective and subjective evaluations than those state-of-the-art denoising techniques.