Efficient local stereo matching algorithm based on fast gradient domain guided image filtering |
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Affiliation: | 1. Information Technologies Institute, Centre for Research and Technology-Hellas, 6th km CharilaouThermi, GR-57001 Thessaloniki, Greece;2. Electronic Engineering and Computer Science Department, Queen Mary University of London, Mile End Road, E1 4NS London, UK |
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Abstract: | 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. |
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Keywords: | Stereo matching Cost aggregation Disparity refinement Guided image filtering |
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