Collocation Method for Multiplicative Noise Removal Model

Mushtaq Ahmad Khan; Zawar Hussain Khan; Haseeb Khan; Sheraz Khan; Suhail Khan

doi:10.22581/muet1982.2004.05

Author(s)

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, Peshawer, 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.

Publication Details

Page(s) 734-743

DOI 10.22581/muet1982.2004.05

Published Journal: Mehran University Research Journal of Engineering and Technology, Volume: 39, Issue: 4, Year: 2020

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Keywords

Mesh-Based Scheme, Speckle Noise, Multiquadric Radial Basis Functions, Meshless Scheme