Non-parametric detection scheme for isotropic image texture with normal increments

We analyze applicability of 2D fractal Brownian motion (fBm) for real-life image textures with respect to two general fBm properties: isotropy and normality of its increments. A non-parametric detection scheme for texture satisfying these two properties is proposed. It is based on Lilliefors test for texture increments normality and Kolmogorov-Smirnov two samples test for equality of distributions of pairs of increments. The scheme is tested against large real-life images database and is shown to detect and remove such image patterns as edges, areas with clipping effects, irregular and anisotropic textures.