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Regularized Fractional Power Parameters for Image Denoising Based on Convex Solution of Fractional Heat Equation

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  • Hamid A. Jalab

Abstract

The interest in using fractional mask operators based on fractional calculus operators has grown for image denoising. Denoising is one of the most fundamental image restoration problems in computer vision and image processing. This paper proposes an image denoising algorithm based on convex solution of fractional heat equation with regularized fractional power parameters. The performances of the proposed algorithms were evaluated by computing the PSNR, using different types of images. Experiments according to visual perception and the peak signal to noise ratio values show that the improvements in the denoising process are competent with the standard Gaussian filter and Wiener filter.

Suggested Citation

  • Hamid A. Jalab, 2014. "Regularized Fractional Power Parameters for Image Denoising Based on Convex Solution of Fractional Heat Equation," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnlaaa:v:2014:y:2014:i:1:n:590947
    DOI: 10.1155/2014/590947
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    References listed on IDEAS

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    1. Ibrahim Karatay & Serife R. Bayramoglu, 2012. "A Characteristic Difference Scheme for Time-Fractional Heat Equations Based on the Crank-Nicholson Difference Schemes," Abstract and Applied Analysis, Hindawi, vol. 2012, pages 1-11, October.
    2. Ibrahim Karatay & Serife R. Bayramoglu, 2012. "A Characteristic Difference Scheme for Time‐Fractional Heat Equations Based on the Crank‐Nicholson Difference Schemes," Abstract and Applied Analysis, John Wiley & Sons, vol. 2012(1).
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