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.

由于具有边缘感知的保留平滑特性，因此广泛研究了基于导引图像滤波（GIF）的成本汇总或视差细化立体匹配算法。但是，GIF会在锐利边缘出现光晕伪像，并且在高分辨率图像上显示出高昂的计算成本。GIF在立体声匹配中的性能将受到上述两个缺陷的限制。为了解决这些问题，提出了一种新颖的快速梯度域导引图像滤波（F-GDGIF）。具体而言，通过将有效的多尺度边缘感知加权合并到GIF中，可以有效缓解光晕伪影。使用这种多尺度加权，可以更好地保留边缘。此外，子采样策略可降低高计算量，从而降低了O（N）至O（N / s ²）（s：子采样率）为了验证算法的有效性，将F-GDGIF分别应用于立体匹配算法中的成本汇总和视差细化。在Middlebury评估基准上的实验表明，与其他基于GIF的方法相比，基于F-GDGIF的立体声匹配方法可以以较低的计算成本生成更多精度的视差图。