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Reseach Article

Spatially Adaptive Image Denoising using Undecimated Directionlet Transform

by Sethunadh R, Tessamma Thomas
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 84 - Number 11
Year of Publication: 2013
Authors: Sethunadh R, Tessamma Thomas
10.5120/14624-2968

Sethunadh R, Tessamma Thomas . Spatially Adaptive Image Denoising using Undecimated Directionlet Transform. International Journal of Computer Applications. 84, 11 ( December 2013), 43-49. DOI=10.5120/14624-2968

@article{ 10.5120/14624-2968,
author = { Sethunadh R, Tessamma Thomas },
title = { Spatially Adaptive Image Denoising using Undecimated Directionlet Transform },
journal = { International Journal of Computer Applications },
issue_date = { December 2013 },
volume = { 84 },
number = { 11 },
month = { December },
year = { 2013 },
issn = { 0975-8887 },
pages = { 43-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume84/number11/14624-2968/ },
doi = { 10.5120/14624-2968 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:00:41.565210+05:30
%A Sethunadh R
%A Tessamma Thomas
%T Spatially Adaptive Image Denoising using Undecimated Directionlet Transform
%J International Journal of Computer Applications
%@ 0975-8887
%V 84
%N 11
%P 43-49
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper a novel de-noising method based on directionlet transform and on sub band adaptive Bayesian threshold is presented. The denoising scheme used in wavelet domain has been extended to the directionlet domain to make the image features to concentrate on fewer coefficients so that more effective thresholding is possible. Here the directionality of the spatially segmented image is first computed using a parameter called directional variance for selecting the optimum direction for decomposing the image using undecimated directionlet transform. The decomposed images with directional energy are used for threshold computation using Bayes scheme. This threshold is then applied to the sub-bands except the LLL subband. The threshold corrected sub-bands with the unprocessed first sub-band are given as input to the inverse directionlet algorithm for getting the de-noised image. Experimental results show that the proposed method outperforms the standard wavelet-based denoising methods in terms of perceptual and numerical estimates.

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Index Terms

Computer Science
Information Sciences

Keywords

Undecimated directionlet transform Multi resolution analysis Directional variance Image Denoising Bayes threshold.