Image Denoising using Variations of Perona-Malik Model with different Edge Stopping Functions

Anisotropic diffusion is used for both image enhancement and denoising. The Perona-Malik model makes use of anisotropic diffusion to filter out the noise. In Perona-Malik model the rate of diffusion is controlled by edge stopping function. The drawback of Perona-Malik model is that the sharp edges and fine details are not preserved well in the denoised image. But the sharp edges and fine details can be preserved well using appropriate edge stopping function. We have analysed the effect of different edge stopping functions in anisotropic diffusion in terms of how efficient they are in preserving edges. We have found that an edge stopping function which stops diffusion from low image gradient onwards well preserves the sharp edges and fine details. This property of an edge stopping function will also result in lower evolution in case of level set methods. But an edge stopping function which stops diffusion from high image gradient onwards will not preserve sharp edges and fine details, since they are blurred due to diffusion. We have also found that low values of gradient threshold parameter used in edge stopping function well preserves the sharp edges and fine details than high values of threshold parameter. By utilizing an edge stopping function which stops diffusion from low image gradient onwards or which has zero or insignificant value at low image gradient, we can well preserve the sharp edges and fine details in the denoised image. (C) 2015 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).