Denoising using linear and nonlinear multiresolutions

Abstract

PurposeTo provide several comparisons between linear and nonlinear approaches in denoising applications.Design/methodology/approachThe 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.FindingsNonlinear 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/implicationsIn 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 implicationsPeople can use the new denoising algorithm presented in the paper.Originality/valueIn this paper nonlinear schemes in the Harten's multiresolution framework that improve the results of the classical linear schemes are presented.