Statistical filtering of raster map images

Filtering of raster map images or more general class of palette-indexed images can be 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 are limited to deal with image with finite color input. In this paper, we further extended the method to a specific continuous-input-finite-output problem, in which the map images are corrupted by additive Gaussian noise instead. This extended method iteratively conducts a fusion procedure based on the probability distribution of pixels´ intensity in RGB space and their conditional probabilities in the local contexts. Experimental results have demonstrated that the proposed algorithm is very efficient in filtering both impulsive and additive Gaussian noise.