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A hybrid denoising algorithm based on shearlet transform method and Yaroslavsky’s filter

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Abstract

In this paper, we have proposed a hybrid denoising algorithm based on combining of the shearlet transform method, as a pre-processing step, with the Yaroslavsky’s filter, as a kernel smoother, on a wide class of images with various properties such as thin features and textures. In the other word, proposed algorithm is a two-step algorithm, where in the first step the image is filtered by shearlet transform method and in the second step the weighted Yaroslavsky’s filter is applied on result of first step. The weight coefficients of the Yaroslavsky’s filter are achieved by pixel similarities in the denoised image from the first step. The theoretical results are confirmed via simulations for 2D images corrupted by additive white Gaussian noise. Experimental results illustrate that proposed hybrid method has good effect on suppressing the pseudo-Gibbs and shearlet-like artifacts can obtain better performance in terms of mean square error (MSE), peak signal to noise ratio (PSNR) and structural similarity (SSIM) index rather than existing state-of-the-art methods.

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Acknowledgments

The authors would like to thank the anonymous referees for their useful suggestions, which significantly clarified the presentation of the paper.

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Authors and Affiliations

  1. Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Tabriz, Tabriz, Iran

    Reza Abazari & Mehrdad Lakestani

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  1. Reza Abazari
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  2. Mehrdad Lakestani
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Correspondence to Reza Abazari.

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Abazari, R., Lakestani, M. A hybrid denoising algorithm based on shearlet transform method and Yaroslavsky’s filter. Multimed Tools Appl 77, 17829–17851 (2018). https://doi.org/10.1007/s11042-018-5648-7

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  • DOI: https://doi.org/10.1007/s11042-018-5648-7

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