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MR Image Denoising by FGMM Clustering of Image Patches

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Image and Graphics (ICIG 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12889))

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Abstract

The general procedure of image denoising is solving an inverse problem with some statistic prior information as the regularization. We propose a novel noise removal method for Magnetic Resonance (MR) images based on the Fuzzy Gaussian Mixture Model (FGMM) Clustering of image patches. In this method, the FGMM, which is an extension of the Gaussian Mixture Model (GMM), has been trained using overlapping clean patches randomly selected from the image database firstly. Then the objective function, which is the sum of the image corruption model and the prior model, is constructed. We optimize the objective using the “Half Quadratic Splitting” method and obtain an expression of iteration to restore the whole image. Finally, we use the proposed method to denoise Magnetic Resonance (MR) images. The experimental results show that the proposed method achieves good performance in MR image denoising.

Supported by Macau Science and Technology development fund (FDCT): FDCT/196/2017/A3.

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Authors and Affiliations

  1. Faculty of Science and Technology, University of Macau, Macau SAR, China

    Zhaoyin Shi & Long Chen

Authors
  1. Zhaoyin Shi
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  2. Long Chen
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Corresponding author

Correspondence to Long Chen .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Tsinghua University, Beijing, China

    Shi-Min Hu

  3. Tampere University, Tampere, Finland

    Moncef Gabbouj

  4. Zhejiang University, Hangzhou, China

    Kun Zhou

  5. Technion – Israel Institute of Technology, Haifa, Israel

    Michael Elad

  6. Tsinghua University, Beijing, China

    Kun Xu

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Shi, Z., Chen, L. (2021). MR Image Denoising by FGMM Clustering of Image Patches. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12889. Springer, Cham. https://doi.org/10.1007/978-3-030-87358-5_48

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  • DOI: https://doi.org/10.1007/978-3-030-87358-5_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87357-8

  • Online ISBN: 978-3-030-87358-5

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