Denoising of natural stochastic colored-textures based on fractional brownian motion model
Ido Zachevsky, Yehoshua Y. Josh Zeevi · 2015
Denoising of Natural Stochastic Colored-Textures (color NST) is of special interest in image processing. Existing algorithms produce over-smoothed images with sharp edges, and do not restore the fine textural color details. We analyze the structure of color NST images and propose a simple model. This model is Gaussian and has a low number of parameters that can be estimated efficiently. A maximum-a-posteriori (MAP) scheme is proposed for patch-wise denoising of color NST. The denoised images exhibit better restored textural details compared to existing algorithms. A boosting algorithm is proposed for denoising of complex images containing both cartoon-type and textural image components.