Statistics of Stochastic Textures: Application in Pattern Analysis and Image Processing

Unlike the well-established fact of non-Gaussian, highly kurtotic, statistical property that characterizes the general class of natural images, a wide class of Natural Stochastic Textures (NST) obeys, to a good approximation, Gaussianity. This is exploited in denoising, resolution enhancement, analysis, modelling and classification of textures and textured images. Denoising is performed by decomposition to cartoon and textural layers. A fractal model is used to restore the latter. Deconvolution is performed via a variational scheme in the frequency domain, using phase and long-range dependency properties of NST.