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Images Denoising with Feature Extraction for Patch Matching in Block Matching and 3D Filtering

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Intelligent Computing Theory (ICIC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8588))

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Abstract

In this paper, we propose a new method for grey scale image denoising. Our method takes advantage of the fact that the mean of the Gaussian white noise is zero. For every patch in the noisy image, we use a line to divide the image into two regions with equal area, and then take the mean of one of the two regions. We select lines with different slopes in order to extract a number of features. We use these extracted features to match the patches in the noisy image. All other steps in our method are the same as those in the standard BM3D. Our experimental results show that our new method outperforms the standard BM3D for (n >120, and they are identical, otherwise.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada, H3G 1M8

    Guangyi Chen

  2. Department of Mechanical and Industrial Engineering, Concordia University, Montreal, Quebec, Canada, H3G 1M8

    Wenfang Xie

  3. State Key Lab. of Virtual Reality Technology and Systems, Beihang University, ZipCode 100191, No 37, Xueyuan Rd., Haidian District, Beijing, P.R. China

    Shu-Ling Dai

Authors
  1. Guangyi Chen
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  2. Wenfang Xie
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  3. Shu-Ling Dai
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Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. Electrical and Electronics Department, Politecnico of Bari, Via Orabona, 4,, 70125, Bari, Italy

    Vitoantonio Bevilacqua

  3. School of Electrical, Computer and Telecommunications Engineering, University of Wollongon, 2522, North Wollongon, NSW, Australia

    Prashan Premaratne

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© 2014 Springer International Publishing Switzerland

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Chen, G., Xie, W., Dai, SL. (2014). Images Denoising with Feature Extraction for Patch Matching in Block Matching and 3D Filtering. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theory. ICIC 2014. Lecture Notes in Computer Science, vol 8588. Springer, Cham. https://doi.org/10.1007/978-3-319-09333-8_43

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  • DOI: https://doi.org/10.1007/978-3-319-09333-8_43

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09332-1

  • Online ISBN: 978-3-319-09333-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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