Edge-aware texture filtering with superpixels constraint

Extracting meaningful structural edges from complex texture images presents a significant challenge. Accurately measuring and differentiating texture information within an image are crucial for efficient texture filtering. While most existing texture filtering methods employ regular rectangular filter windows, the irregularity inherent in textures and structures can limit measurement accuracy, reducing the effectiveness of texture filtering. To address this problem, we propose an edge-aware texture filtering method that integrates superpixels. By employing patch shift, our filter constructs an edge-aware filtering region with superpixels constraint. This region includes pixels with minimal differences and similar texture characteristics. Utilizing the perceptual properties of superpixels for irregular edges enhances texture measurement, thereby improving the quality of texture filtering. Experimental results demonstrate that the proposed method outperforms existing techniques, yielding superior filtering outcomes. The source code is available at: https://github.com/kxZhang1016/EATFS.