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.

Link: https://www.sciencedirect.com/science/article/pii/S0923596521001181

Result: https://vision.middlebury.edu/stereo/eval3/

@article{yuan2021efficient, title={Efficient local stereo matching algorithm based on fast gradient domain guided image filtering}, author={Yuan, Weimin and Meng, Cai and Tong, Xiaoyan and Li, Zhaoxi}, journal={Signal Processing: Image Communication}, volume={95}, pages={116280}, year={2021}, publisher={Elsevier} }