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Multiplicative Noise Removal via Nonlocal Similarity-Based Sparse Representation
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  • 作者:Lixia Chen ; Xujiao Liu ; Xuewen Wang…
  • 关键词:Multiplicative noise removal ; Dictionary learning ; Nonlocal similarity ; Surrogate function ; Iterative shrinkage
  • 刊名:Journal of Mathematical Imaging and Vision
  • 出版年:2016
  • 出版时间:February 2016
  • 年:2016
  • 卷:54
  • 期:2
  • 页码:199-215
  • 全文大小:4,822 KB
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  • 作者单位:Lixia Chen (1) (2) (3)
    Xujiao Liu (1)
    Xuewen Wang (2) (3) (4)
    Pingfang Zhu (1)

    1. School of Mathematics and Computing Science, Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, Guilin, China
    2. Guangxi Experiment Center of Information Science, Guilin, China
    3. Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics, Guilin University of Electronic Technology, Guilin, China
    4. School of Computer Science and Engineering, Guilin University of Electronic Technology, Guilin, China
  • 刊物类别:Computer Science
  • 刊物主题:Computer Imaging, Vision, Pattern Recognition and Graphics
    Image Processing and Computer Vision
    Artificial Intelligence and Robotics
    Automation and Robotics
  • 出版者:Springer Netherlands
  • ISSN:1573-7683
文摘
Based on the sparse representation and by connecting the local and nonlocal regularizer, we proposed a new model to remove multiplicative noise in this paper. We first translated the multiplicative noise into additive noise by a logarithmic transformation, and then introduced a local regularizer based on dictionary learning and a nonlocal regularizer with nonlocal similarity to capture texture and edge information. A surrogate function-based iterative shrinkage algorithm was designed to solve the proposed model. Finally, the solution was transformed back into the real domain via an exponential function and bias correction. Experiments show that the denoised results of our model outperform state-of-the-art algorithms in terms of objective indices and subjective visual effect. Keywords Multiplicative noise removal Dictionary learning Nonlocal similarity Surrogate function Iterative shrinkage

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