Texture enhancement algorithm based on fractional differential mask of adaptive non-integral step

Abstract

Image texture enhancement is an important topic in computer graphics, computer vision and pattern recognition. By applying the fractional differential principle to analyze texture characteristics, a new fractional differential mask with adaptive non-integral step is proposed in this paper to enhance texture images. A non-regular self-similar support region is constructed based on a local texture similarity measure, which can exclude low-correlated pixels and noise. Then, through applying sub-pixel division and introducing a local linear piecewise model to estimate the gray value in between the pixels, the resulting nonintegral steps can improve the characterization of self-similarity that is inherent in digital images. Finally, the non-regular fractional differential mask which incorporates adaptive nonintegral step is constructed. Experimental results show that, for rich-grained digital images, the capability of improved self-similarity and texture characterization based on our proposed approach leads to improved image enhancement results when compared with conventional approaches.