Model-based color natural stochastic textures processing and classification

Processing and classification of color Natural Stochastic Textures (NST) are of importance in various facets of image restoration, enhancement and pattern recognition. Existing denoising and deblurring algorithms produce over-smoothed images with sharp edges, but do not restore the fine textural color details. A recently proposed color-NST model, endowed with a small number of parameters, is extended and used for deblurring and denoising via a linear maximum-a-posteriori (MAP) scheme. The restored images exhibit better textural details than those recovered by other algorithms. Orientation and coherence-based features are combined with the color-NST model for classification, showing improvement over algorithms implementing only isotropic and color-based features.