Model-based color natural stochastic textures processing and classification
Ido Zachevsky, Yehoshua Y. Josh Zeevi · 2015
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.