Image driven isotropic difusivity and complementary regularization approach for image denoising problem

Memoona Pirzada; Khuda Bux Amur; Muzafar Bashir Arain; Rajab Ali Malookani

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Image driven isotropic difusivity and complementary regularization approach for image denoising problem

Author(s)

Memoona Pirzada Department of Mathematics and Statistics, Quaid-e-Awam, University of Engineering, Science and Technology, Nawabshah, Pakistan

Khuda Bux Amur Department of Mathematics and Statistics, Quaid-e-Awam, University of Engineering, Science and Technology, Nawabshah, Pakistan

Muzafar Bashir Arain Department of Mathematics and Statistics, Quaid-e-Awam, University of Engineering, Science and Technology, Nawabshah, Pakistan

Rajab Ali Malookani Department of Mathematics and Statistics, Quaid-e-Awam, University of Engineering, Science and Technology, Nawabshah, Pakistan

Abstract

We present the idea of image driven isotropic difusivity along with complementary regularization for image denoising problem. The method is based on the optimization of a quadratic function in L2 norm. The minimization of the energy functional leads to the Partial Diferential equation (PDE)-based problem. We are looking for a steady state solution of equivalent time dependent problem. We discretize the problem with standard finite diferences. The steady-state numerical solution of the time dependent problem leads to the iterative procedure, which allow to compute a regularized version of the solution as a denoised image. We have applied our designed model on synthetic as well as real images. The numerous experiments have been conducted to analyse the performance of the method for the diferent choices of scaling parameters. From the quality of the obtained results and comparative study it is observed that the proposed model performs well as compared to well existing methods.

Publication Details

Page(s) 1-13

DOI DOI not available

Published Journal: VFAST Transactions on Mathematics, Volume: 10, Issue: 1, Year: 2022

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Keywords

Optimization, regularization, Subject Classification, Image Denoising, Partial Diferential Equation, Difusion, Applied Mathematics, Mathematical Image Processing

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