Collocation Method for Multiplicative Noise Removal Model

  • Mushtaq Ahmad Khan Department of Electrical Engineering, University of Engineering and Technology, Mardan, Pakistan.
  • Zawar Hussain Khan Department of Electrical Engineering, University of Engineering and Technology, Peshawar, Pakistan.
  • Haseeb Khan Department of Electrical Engineering, University of Engineering and Technology, Peshawer, Pakistan.
  • Sheraz Khan Department of Electrical Engineering, University of Engineering and Technology, Mardan, Pakistan.
  • Suhail Khan Department of Computer and Software Engineering, University of Engineering and Technology, Mardan, Pakistan.

Abstract

Image denoising is a fundamental problem in both image processing and computer vision with numerous applications. It can be formulated as an inverse problem. Variational methods are commonly used to solve noise removal problems. The Total Variation (TV) regularization has evolved from an image denoising method for images corrupted with multiplicative noise into a more general technique for inverse problems such as denoising, deblurring, blind deconvolution, and inpainting, which also encompasses the Impulse, Poisson, Speckle, and mixed noise models. Multiplicative noise removal based on TV regularization has been widely researched in image science. In multiplicative noise problems, original image is multiplied by a noise rather than added to the original image. This article proposes a novel meshless collocation technique for the solution of a model having multiplicative noise. This technique includes TV and local collocation along with Multiquadric Radial Basis Function (MQ-RBF) for the solution of associated Euler-Lagrange equation for restoring multiplicative noise from digital images. Numerical examples demonstrate that the proposed algorithm is able to preserve small image details while the noise in the homogeneous regions is removed sufficiently. As a consequence, our method yields better denoised results than those of the current state of the art methods with respect to the Peak-Signal to Noise Ratio (PSNR) values.

Published
Oct 1, 2020
How to Cite
KHAN, Mushtaq Ahmad et al. Collocation Method for Multiplicative Noise Removal Model. Mehran University Research Journal of Engineering and Technology, [S.l.], v. 39, n. 4, p. 734-743, oct. 2020. ISSN 2413-7219. Available at: <https://publications.muet.edu.pk/index.php/muetrj/article/view/1823>. Date accessed: 20 oct. 2020. doi: http://dx.doi.org/10.22581/muet1982.2004.05.
This is an open Access Article published by Mehran University of Engineering and Technolgy, Jamshoro under CCBY 4.0 International License