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Image denoising based on adaptive nonlinear diffusion in wavelet domain

[+] Author Affiliations
Ajay K. Mandava

University of Nevada, Las Vegas, Electrical and Computer Engineering, 4505 Maryland Parkway, Box 454026, Las Vegas, Nevada 89154-4026

Emma E. Regentova

University of Nevada, Las Vegas, Electrical and Computer Engineering, 4505 Maryland Parkway, Box 454026, Las Vegas, Nevada 89154-4026

J. Electron. Imaging. 20(3), 033016 (September 14, 2011). doi:10.1117/1.3628671
History: Received February 23, 2011; Revised July 26, 2011; Accepted August 05, 2011; Published September 14, 2011; September 22, 2011; Online September 14, 2011
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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.

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Citation

Ajay K. Mandava and Emma E. Regentova
"Image denoising based on adaptive nonlinear diffusion in wavelet domain", J. Electron. Imaging. 20(3), 033016 (September 14, 2011). ; http://dx.doi.org/10.1117/1.3628671


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