By Topic

The bidimensional empirical mode decomposition with 2D-DWT for gaussian image denoising

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Ben Arfia, F. ; Comput. Eng. Syst. Design Lab. (CES), Nat. Eng. Sch. of Sfax, Sfax, Tunisia ; Sabri, A. ; Ben Messaoud, M. ; Abid, M.

This paper presents a new adaptive approach for image denoising with Gaussian noise based on a combination of the Bidimensional Empirical Mode Decomposition (BEMD) and the the discrete wavelet transforms (DWT). The BEMD is an auto-adaptive method for the analysis of nonlinear or non-stationary signals and images. The input image is decomposed into several modes called Intrinsic Mode Functions (IMFs), which show new characteristics of the images. In this paper, we propose to apply the BEMD approach in the image denoising domain by using the first IMF to reduce the Gaussian noise in blurred images. After that, we combine the BEMD with the DWT to improve the BEMD denoising method. Finally, we show the influence of the number of IMFs filtered with the DWT on the visual quality in term of PSNR of the denoised image.

Published in:

Digital Signal Processing (DSP), 2011 17th International Conference on

Date of Conference:

6-8 July 2011