A Nonlinear Total Variation-Based Denoising Method With Two Regularization Parameters

Author

Drapaca, Corina S.

Author_Institution

Dept. of Eng. Sci. & Mech., Pennsylvania State Univ., University Park, PA

Volume

56

Issue

3

fYear

2009

fDate

3/1/2009 12:00:00 AM

Firstpage

582

Lastpage

586

Abstract

The aim of the present paper is to study the effect of the regularization parameter used in the numerical implementation of the Rudin-Osher-Fatemi denoising model. By using two different regularization parameters in the numerical scheme of the Rudin-Osher-Fatemi model, we will show experimentally that when a particular relationship between the sizes of these parameters holds, the quality of the denoised image and the speed of convergence of the numerical scheme are both much improved in comparison with the classic numerical scheme of the Rudin-Osher-Fatemi model where only one regularization parameter is used.

Keywords

image denoising; medical image processing; Rudin-Osher-Fatemi denoising model; nonlinear total variation-based denoising; regularization parameters; Biomedical imaging; Convergence of numerical methods; Electrical capacitance tomography; Equations; Gaussian noise; Image denoising; Image edge detection; Image reconstruction; Lagrangian functions; Mathematical model; Noise level; Noise reduction; Gradient descent method; image denoising; total variation; Algorithms; Image Processing, Computer-Assisted; Models, Theoretical; Phantoms, Imaging;

fLanguage

English

Journal_Title

Biomedical Engineering, IEEE Transactions on

Publisher

ieee

ISSN

0018-9294

Type

jour

DOI

10.1109/TBME.2008.2011561

Filename

4760229