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Study on Image Denoising Method Based on Multiple Parameter Shrinkage Function

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Abstract

In transform based image denoising methods, how to modify the transform coefficients is an important problem. In wavelet image denoising, two-dimensional tensor product wavelet has isotropy, with poor selectivity, making it difficult to describe the high dimensional geometric features of images. With the development of multi-scale transform, Contourlet transform is emerging prominently. In this study, the advantages of soft threshold and hard threshold shrinkage functions are combined and a multiple parameter shrinkage function (MPSF) is proposed for image denoising. To verify the effectiveness of MPSF, it is used to denoise images polluted by Gaussian white noise. Experimental results show that the proposed shrinkage function is effective, and the denoised images have satisfactory visual effect, with significantly improved image quality metrics such as peak signal to noise ratio.

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Acknowledgements

The authors acknowledge the Fundamental Research Funds for the Central Universities (No. 2016MS151).

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Authors and Affiliations

  1. School of Control and Computer Engineering, North China Electric Power University, Baoding, China

    Wei Xiong, Ze Wang & Hejin Yuan

  2. Japan–China AI-IoT Industry Alliance, Tokyo, Japan

    Jin Liu

Authors
  1. Wei Xiong
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  2. Ze Wang
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  3. Hejin Yuan
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  4. Jin Liu
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Correspondence to Wei Xiong.

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Xiong, W., Wang, Z., Yuan, H. et al. Study on Image Denoising Method Based on Multiple Parameter Shrinkage Function. Wireless Pers Commun 102, 3079–3088 (2018). https://doi.org/10.1007/s11277-018-5327-z

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  • DOI: https://doi.org/10.1007/s11277-018-5327-z

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