Denoising using linear and nonlinear multiresolutions
Abstract
Purpose
To provide several comparisons between linear and nonlinear approaches in denoising applications.
Design/methodology/approach
The comparison is based on the peak signal noise ratio (PSNR) image quality measure. Which one of the algorithms gives higher PSNR and then denoises more the original picture is studied.
Findings
Nonlinear reconstruction operators can improve the accuracy of the prediction in the vicinity of isolated singularities. A better treatment of the singularities corresponding to the image edges and, therefore, an improvement on the sparsity of the multiresolution representation of images are then expected.
Research limitations/implications
In this paper the point‐value framework is considered. Other frameworks, as the cell‐average discretization, are more suitable for image processing where noise and texture appear. But, the point value schemes can be adapted to the cell‐average discretization using primitive function.
Practical implications
People can use the new denoising algorithm presented in the paper.
Originality/value
In this paper nonlinear schemes in the Harten's multiresolution framework that improve the results of the classical linear schemes are presented.
Keywords
Citation
Amat, S., Cherif, H. and Carlos Trillo, J. (2005), "Denoising using linear and nonlinear multiresolutions", Engineering Computations, Vol. 22 No. 7, pp. 877-891. https://doi.org/10.1108/02644400510619567
Publisher
:Emerald Group Publishing Limited
Copyright © 2005, Emerald Group Publishing Limited