Efficient local stereo matching algorithm based on fast gradient domain guided image filtering

Guided image filtering (GIF) based cost aggregation or disparity refinement stereo matching algorithms are studied extensively owing to the edge-aware preserved smoothing property. However, GIF suffers from halo artifacts in sharp edges and shows high computational costs on high-resolution images. The performance of GIF in stereo matching would be limited by the above two defects. To solve these problems, a novel fast gradient domain guided image filtering (F-GDGIF) is proposed. To be specific, halo artifacts are effectively alleviated by incorporating an efficient multi-scale edge-aware weighting into GIF. With this multi-scale weighting, edges can be preserved much better. In addition, high computational costs are cut down by sub-sampling strategy, which decreases the computational complexity from O(N) to O(N/s2) (s: sub-sampling ratio) To verify the effectiveness of the algorithm, F-GDGIF is applied to cost aggregation and disparity refinement in stereo matching algorithms respectively. Experiments on the Middlebury evaluation benchmark demonstrate that F-GDGIF based stereo matching method can generate more accuracy disparity maps with low computational cost compared to other GIF based methods. •By incorporating a multi-scale edge-aware weighting and sub-sampling strategy into GIF, a novel fast gradient domain guided image filter (F-GDGIF) is proposed, which achieves better edge-aware performance with a faster execution time.•The proposed F-GDGIF is employed in cost aggregation. Due to its advantages of edge preserved smoothing property and low computational complexity, F-GDGIF based cost aggregation method achieves better performance compared to other GIF based methods.•Apart from cost aggregation, F-GDGIF is also adopted in disparity refinement and achieved better results compared to GIF based method.