Robust double-weighted guided image filtering

Guided image filter (GIF) is an edge-preserving smoothing operator with low time complexity; However, it does not preserve sharp edges, and therefore exhibits halo artifacts caused by the edge blurring. To address this problem, weighted versions of the GIF have been proposed in the literature. Along the same line of research, we present a robust double-weighted guided image filter (RDWGIF) by incorporating the robust edge-aware weighting (REAW) and the mollifier from Sobolev space theory into the cost function of the GIF. The REAW is proposed based on a new measure called Maximum Neighbor Difference (MND) and the Tukey's biweight from robust statistics. To illustrate its effectiveness in preserving sharp edges and reducing halo artifacts, the proposed filter was applied to edge aware smoothing, image denoising, single image detail enhancement, tone mapping of high dynamic range images, and texture removal smoothing. The experimental results show that the RDWGIF outperforms previous weighted versions of the GIF by qualitative comparison and quantitative evaluation. (c) 2022 Published by Elsevier B.V.