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Journal of Electronic Imaging

Image denoising based on adaptive nonlinear diffusion in wavelet domain
Author(s): Ajay K. Mandava; Emma Regentova
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Paper Abstract

In this paper, we propose a context adaptive nonlinear diffusion method for image denoising in wavelet domain which we call context based diffusion in stationary wavelet domain (SWCD). In diffusing detail coefficients, the method adapts to the local context such that strong edges are preserved and smooth regions are diffused in a greater extent. The local context which is derived directly from the transform energies at scales 1 and 2 of two-level stationary wavelet transform (SWT) controls the diffusion. The shift invariance of SWT contributes to the performance of the method. The experiment is conducted on a number of benchmark images and compared to recently developed denoising methods which explore the adaptation concept for wavelet shrinkage and diffusion. A comparison is performed also to a method of diffusing both approximation and detail coefficients. The proposed SWCD method outperforms recently proposed adaptive shrinkage and adaptive diffusion, particularly at high noise levels. The method is computationally efficient due to the Haar wavelet and fast convergence attained due to exploiting the context information.

Paper Details

Date Published: 1 July 2011
PDF: 8 pages
J. Electron. Imag. 20(3) 033016 doi: 10.1117/1.3628671
Published in: Journal of Electronic Imaging Volume 20, Issue 3
Show Author Affiliations
Ajay K. Mandava, Univ. of Nevada, Las Vegas (United States)
Emma Regentova, Univ. of Nevada, Las Vegas (United States)

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