Sun Zhonggui, Han Bo, Li Jie, Zhang Jin, Gao Xinbo
IEEE Trans Image Process. 2019 Jul 19. doi: 10.1109/TIP.2019.2928631.
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.
由于其局部特性，引导图像滤波器（GIF）在边缘附近通常会出现光晕伪影。为了弥补这一不足，最近提出了一种加权引导图像滤波器（WGIF），通过在滤波过程中加入边缘感知加权。它兼具局部和全局操作的优点，在边缘保留方面表现更好。然而，边缘方向作为引导图像的一个重要属性，在这些引导滤波器中并未得到充分考虑。为了克服这一缺点，我们提出了一种新型的GIF，它可以更充分地利用边缘方向。具体而言，我们利用导向核自适应地学习方向，并将学习结果纳入滤波过程以改善滤波器的性能。理论分析表明，该方法在有效保留边缘和减少光晕伪影方面具有更强的性能。通过包括边缘感知平滑、细节增强、去噪和去雾在内的全面实验也得出了类似的结论。
