Image denoising using modified nonlinear diffusion approach

Akhilesh R. Upadhyay; Sanjay N. Talbar; Trimbak R. Sontakke

doi:10.1117/12.641627

17 January 2006 Image denoising using modified nonlinear diffusion approach

Akhilesh R. Upadhyay, Sanjay N. Talbar, Trimbak R. Sontakke

Proceedings Volume 6059, Image Quality and System Performance III; 60590B (2006) https://doi.org/10.1117/12.641627
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

Abstract

Partial Differential Equation (PDE) based, non-linear diffusion approaches are an effective way to denoise the images. In this paper, the work is extended to include anisotropic diffusion, where the diffusivity is a tensor valued function, which can be adapted to local edge orientation. This allows smoothing along the edges, but not perpendicular to it. The diffusion tensor is a function of differential structure of the evolving image itself. Such a feedback leads to nonlinear diffusion filters. It shows improved performance in the presence of noise. The original anisotropic diffusion algorithm updates each point based on four nearest-neighbor differences, the progress of diffusion results in improved edges. In the proposed method the edges are better preserved because diffusion is controlled by the gray level differences of diagonal neighbors in addition to 4 nearest neighbors using coupled PDF formulation. The proposed algorithm gives excellent results for MRI images, Biomedical images and Fingerprint images with noise.

Citation Download Citation

Akhilesh R. Upadhyay, Sanjay N. Talbar, and Trimbak R. Sontakke "Image denoising using modified nonlinear diffusion approach", Proc. SPIE 6059, Image Quality and System Performance III, 60590B (17 January 2006); https://doi.org/10.1117/12.641627

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KEYWORDS

Diffusion

Nonlinear filtering

Signal to noise ratio

Anisotropic diffusion

Image enhancement

Magnetic resonance imaging

Linear filtering

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