Adaptive filtering of raster map images using optimal context selection

Filtering of raster map images or more general class of palette-indexed images is considered as a discrete denoising problem with finite color output. Statistical features of local context are used to avoid damages of some specific but frequently occurring contexts caused by conventional filters. Several context-based approaches have been developed using either fixed context templates or context tree modeling. However, these algorithms fail to reveal the local geometrical structures when the underlying contexts are also contaminated. To address this problem, we propose a novel context-based voting method to identify the possible noisy pixels, which are excluded in the context selection and optimization. Experimental results show that the proposed context based filtering outperforms all other existing filters both for impulsive and Gaussian additive noise.