Due to its local property, guided image filter (GIF) generally suffers from halo artifacts near edges. To make up for the deficiency, a weighted guided image filter (WGIF) was proposed recently by incorporating an edge-aware weighting into the filtering process. It takes the advantages of local and global operations, and achieves better performance in edge-preserving. However, edge direction, a vital property of the guidance image, is not considered fully in these guided filters. In order to overcome the drawback, we propose a novel version of GIF, which can leverage the edge direction more sufficiently. In particular, we utilize the steering kernel to adaptively learn the direction and incorporate the learning results into the filtering process to improve the filter’s behavior. Theoretical analysis shows that the proposed method can get more powerful performance with preserving edges and reducing halo artifacts effectively. Similar conclusions are also reached through the thorough experiments including edge-aware smoothing, detail enhancement, denoising and dehazing.

Non-Local Euclidean Medians (NLEM) has recently been proposed and shows more effective than Non-Local Means (NLM) in removing heavy noise. In this letter, we find the inconsistency between the two dissimilarity measures in NLEM can affect its robustness, thus develop an improved version (INLEM) to compensate such an inconsistency. Further, we provide a concise convergence proof for the iterative algorithm used in both NLEM and INLEM. Finally, our experiments on synthetic and natural images show that INLEM achieves encouraging results.