ON THE STATISTICS OF NATURAL STOCHASTIC TEXTURES AND THEIR APPLICATION IN IMAGE PROCESSING

Statistics of natural images has become an important subject of research in recent years. The highly kurtotic, nonGaussian, statistics known to be characteristic of many natural images are exploited in various image processing tasks. In this paper, we focus on natural stochastic textures (NST) and substantiate our finding that NST have Gaussian statistics. Using the well-known statistical self-similarity property of natural images, exhibited even more profoundly in NST, we exploit a Gaussian self-similar process known as the fractional Brownian motion, to derive a fBm-PDE-based single-image superresolution scheme for textured images. Using the same process as a prior, we also apply it in denoising of NST.