Anisotropic Guided Filtering

The guided filter and its derivatives have been widely employed in many image processing and computer vision applications due to their low complexity and good edge-preservation properties. Despite this success, these variants are unable to handle more aggressive filtering strengths leading to the manifestation of “detail halos”. These existing filters also perform poorly when the input and guide images have structural inconsistencies. We show that these limitations are due to the guided filter operating as a variable-strength locally-isotropic filter that, in effect, acts as a weak anisotropic filter on the image. Our analysis shows that this behaviour stems from the use of unweighted averaging in the final steps of many guided filter variants. We propose a novel filter, the Anisotropic Guided Filter (AnisGF), that utilises weighted averaging to achieve maximum diffusion while preserving strong edges in the image. The proposed weights are optimised to achieve strong anisotropic filtering while maintaining a low computational cost. Tests show that the proposed method addresses the presence of detail halos and the handling of inconsistent structures found in previous variants of the guided filter. Furthermore, experiments in scale-aware filtering, detail enhancement, texture removal, and chroma upsampling demonstrate the improvements brought about by the technique.