Adaptive Context-Tree-Based Statistical Filtering for Raster Map Image Denoising

Filtering of raster map images is chosen as a case study of a more general class of palette-indexed images for the denoising problem of images with a discrete number of output colors. Statistical features of local context are analyzed to avoid damage to pixel-level patterns, which is frequently caused by conventional filters. We apply a universal statistical filter using context-tree modeling via a selective context expansion capturing those pixel combinations that are present in the image. The selective context expansion makes it possible to use a much larger spatial neighborhood, with a feasible time and memory complexity, than fixed-size templates. We improve the existing context-tree approaches in two aspects: Firstly, in order to circumvent the context contamination problem, a context-merging strategy is applied where multiple similar contexts are considered in the conditional probability estimation procedure. Secondly, we study a specific continuous-input-finite-output problem in which the map images are corrupted by additive Gaussian noise. Performance comparisons with competitive filters demonstrate that the proposed algorithm provides robust noise filtering performance and good structure preservation in all test cases without any a priori information on the statistical properties of the noise.