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Fractional Conway Polynomials for Image Denoising with Regularized Fractional Power Parameters

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Abstract

Image denoising is a significant image processing problem that is difficult to study. The use of fractional masks based on fractional calculus (integral and differential) operators has increased for image denoising. This paper proposes an image denoising algorithm that is based on the generalization of fractional Conway polynomials with regularized fractional power parameters. We operate the structures of fractional masks (differential and integral) by using \(n \times n\) processing masks on eight directions. The performance of the proposed algorithm is evaluated on the basis of visual perception and peak signal-to-noise ratio (PSNR). Theoretical analysis and experimental results demonstrate that the improvements achieved according to visual perception and PSNR values are comparable with Gaussian and Wiener filters.

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Acknowledgments

The authors would like to thank the reviewers for their comments. This research has been funded by university of Malaya, HIR Project UM.C/625/1/HIR/132.

Conflict of interest

The authors declare that they have no competing interests.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Hamid A. Jalab

  2. Institute of Mathematical Sciences, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Rabha W. Ibrahim

Authors
  1. Hamid A. Jalab
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  2. Rabha W. Ibrahim
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Correspondence to Hamid A. Jalab.

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Jalab, H.A., Ibrahim, R.W. Fractional Conway Polynomials for Image Denoising with Regularized Fractional Power Parameters. J Math Imaging Vis 51, 442–450 (2015). https://doi.org/10.1007/s10851-014-0534-z

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  • DOI: https://doi.org/10.1007/s10851-014-0534-z

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