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Adaptive image denoising based on support vector machine and wavelet description

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Abstract

Adaptive image denoising method decomposes the original image into a series of basic pattern feature images on the basis of wavelet description and constructs the support vector machine regression function to realize the wavelet description of the original image. The support vector machine method allows the linear expansion of the signal to be expressed as a nonlinear function of the parameters associated with the SVM. Using the radial basis kernel function of SVM, the original image can be extended into a MEXICAN function and a residual trend. This MEXICAN represents a basic image feature pattern. If the residual does not fluctuate, it can also be represented as a characteristic pattern. If the residuals fluctuate significantly, it is treated as a new image and the same decomposition process is repeated until the residuals obtained by the decomposition do not significantly fluctuate. Experimental results show that the proposed method in this paper performs well; especially, it satisfactorily solves the problem of image noise removal. It may provide a new tool and method for image denoising.

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Acknowledgements

This work was supported by the National Science Foundation Project of P. R. China (nos. 61501026, 61701188 and 61272506).

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Authors and Affiliations

  1. School of Physics and Electronic Electrical Engineering, Huaiyin Normal University, Huaian, 223001, China

    Feng-Ping An

  2. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Feng-Ping An & Xian-Wei Zhou

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  1. Feng-Ping An
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  2. Xian-Wei Zhou
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Correspondence to Feng-Ping An.

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An, FP., Zhou, XW. Adaptive image denoising based on support vector machine and wavelet description. Opt Rev 24, 660–667 (2017). https://doi.org/10.1007/s10043-017-0360-9

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  • DOI: https://doi.org/10.1007/s10043-017-0360-9

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