Image denoising by exploring the context information in the wavelet domain

Authors:
Ajay Kumar Mandava;Emma E. Regentova;Markus Berli
Affiliations:
Electrical and Computer Engineering, University of Nevada, Las Vegas, NV;Electrical and Computer Engineering, University of Nevada, Las Vegas, NV;Desert Research Institute, Las Vegas, NV
Venue:
ECS'10/ECCTD'10/ECCOM'10/ECCS'10 Proceedings of the European conference of systems, and European conference of circuits technology and devices, and European conference of communications, and European conference on Computer science
Year:
2010

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Abstract

Traditional diffusivity based denoising models detect edges by the gradients of images, and thus are easily affected by noise. In this paper, we introduce a nonlinear diffusion denoising method based on the wavelet domain diffusivity model and context information. The shift-invariant property of the stationary wavelet transform makes it suitable for edge detection and derivation of texture information. In the proposed diffusion model, the modulus of gradient in a diffusivity function is substituted by the modulus of a wavelet detail coefficient. The diffusion of a wavelet coefficient is performed based on the information about the energy of the transform in a local neighborhood of coefficients across the scales. It is shown that the new model has better noise suppression and better perceptual quality power for high levels of noise. Objectively results are evaluated based on PSNR and Laplacian mean-square error (LMSE) metrics.