Statistical filtering of raster map images

Abstract

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.

Adaptive filtering of raster map images using optimal context selection

“…This formula is similar to the energy function in Markov random fields, but the term neighborhood homogeneity is replaced by the conditional probability of the local context. The color components in color palette and the mean variance are updated following with the minimization step, see [13] for more details.…”

“…Thus, we need to avoid misclassifying pixels caused by conventional color quantization. A novel iterative algorithm is proposed in [13] to optimize both the estimation of the indexed image and its color palette. For each pixel x, both the distance between RGB color vector to its corresponding component in the color palette, and its conditional probability of local context (estimated in Section 2.2-2.4) are taken into account as follows:…”

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

“…The proposed method is introduced in Section II, experimental results are reported in Section III, and finally, conclusions are drawn in Section IV. A preliminary version of this paper has been presented at ICME [30].…”