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Abstract and Applied Analysis
Volume 2014, Article ID 590947, 8 pages
Research Article

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

Faculty of Computer Science and Information Technology, Multimedia Unit, University of Malaya, 50603 Kuala Lumpur, Malaysia

Received 11 February 2014; Revised 8 April 2014; Accepted 12 May 2014; Published 25 May 2014

Academic Editor: Dumitru Baleanu

Copyright © 2014 Hamid A. Jalab. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.