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1 July 2011 Image denoising based on adaptive nonlinear diffusion in wavelet domain
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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.
©(2011) Society of Photo-Optical Instrumentation Engineers (SPIE)
Ajay K. Mandava and Emma Regentova "Image denoising based on adaptive nonlinear diffusion in wavelet domain," Journal of Electronic Imaging 20(3), 033016 (1 July 2011).
Published: 1 July 2011


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