Heterogeneity-driven hybrid denoising

A filter aimed at denoising should strongly smooth uniform regions, while preserving edges. On textured areas, the filter should attain a compromise to achieve some enhancement without destroying useful information. Filtering performances, however, locally depend on the statistical characteristics of the imaged signal, which can be embodied by the concept of local heterogeneity. It is shown that statistically homogeneous regions originate clusters in the scatterplot of standard deviation to mean. Textured regions yield scatterpoints spread apart to a larger extent. Edges produce outliers. Thus, the homogeneity may be locally measured from the joint PDF of estimated local standard deviation to estimated local mean. For each pixel having a measured local mean and a measured standard deviation, a point is detected in the PDF plane: the corresponding density is taken as a measure of homogeneity of that pixel. In this work a hybrid filter, i.e., a set of filters is considered, the denoising capability of each of which depends on the degree of local homogeneity. Images are individually processed by each filter, and the filtered image is obtained by switching among such channels at each pixel position, based on thresholding a heterogeneity feature in order to identify a number of classes, for each of which the noise-free image signal is best estimated by one of the filters. Visual judgments on simulated noisy images agree with this tendency.