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.
Mushtaq Ahmad Khan, Suhail Khan, Sheraz Khan , Haseeb Khan, Zawar Hussain Khan. (2020) Collocation Method for Multiplicative Noise Removal Model , Mehran University Research Journal of Engineering & Technology, Volume 39, Issue 4.
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