Improved Bilateral Texture Filtering with Edge-aware Measurement.

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Texture filtering depends on high-quality texture measurement to separate structures from textures. However, the existing methods employ axis-aligned box windows for texture measurement, which may cover different texture regions, and so lowering the measurement quality because structure edges are not always parallel to the axes. Additionally, the existing texture measurements consider intensity contrast at the pixel level and do not account for the linear characteristics of structure edges in filtering windows; thus, their measurement effectiveness is limited. This results in a dilemma for texture filtering. Large-scale textures are not smoothed using smaller windows, while small structures are removed using larger windows. In this paper, we present edge-aware measures to improve texture measurement. Edge-aware windows are constructed such that each window is inside a texture region to the greatest extent possible, and the linear characteristics of structure edges are accounted for in the texture measurement. Furthermore, we use large box windows for texture filtering and long and narrow edge-aware small windows for texture measurement to filter out large-scale textures while preserving small structures. The experimental results show improved texture filtering with our method compared with existing methods.