Automatic contrast parameter estimation in anisotropic diffusion for image restoration

Abstract

Anisotropic diffusion is used widely in image processing for edge preserving filtering and image smoothing tasks. One of the important class of such model is by Perona and Malik (PM) who used a gradient based diffusion to drive smoothing along edges and not across it. The contrast parameter used in the PM method needs to be carefully chosen to obtain optimal denoising results. Here we consider a local histogram based cumulative distribution approach for selecting this parameter in a data adaptive way so as to avoid manual tuning. We use spatial smoothing based diffusion coefficient along with adaptive contrast parameter estimation for obtaining better edge maps. Moreover, experimental results indicate that this adaptive scheme performs well for a variety of noisy images and comparison results indicate we obtain better peak signal to noise ratio and structural similarity scores with respect to fixed constant parameter values.