Adaptive Amplification of Image Texture Boundaries

The texture boundaries normally have frequent co-occurrence in natural images, but existing image enhancement techniques for the most parts focus on sharpening the edges, i.e., intensity discontinuities. Moreover, these approaches often suffer from noise over-emphasis and extra artifact production. In this paper, we propose an adaptive texture boundary boosting algorithm. The proposal exploits the co-occurrence filter for dual-layer decomposition and pixel-wise amplification factor calculation for image synthesis. This leads to a various-scale enhancement framework, which allows our method to only highlight those co-occurrent features while avoiding unsalient edges and noise, gradient reversals, as well as halos. Both subjective and objective comparisons using different detail stretching schemes demonstrate the effectiveness of the presented approach.