Proceedings ArticleDOI
Constant Time Weighted Median Filtering for Stereo Matching and Beyond
Ziyang Ma,Kaiming He,Yichen Wei,Jian Sun,Enhua Wu +4 more
- pp 49-56
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TLDR
It is discovered that with this refinement, even the simple box filter aggregation achieves comparable accuracy with various sophisticated aggregation methods (with the same refinement), revealing that the previously overlooked refinement can be at least as crucial as aggregation.Abstract:
Despite the continuous advances in local stereo matching for years, most efforts are on developing robust cost computation and aggregation methods. Little attention has been seriously paid to the disparity refinement. In this work, we study weighted median filtering for disparity refinement. We discover that with this refinement, even the simple box filter aggregation achieves comparable accuracy with various sophisticated aggregation methods (with the same refinement). This is due to the nice weighted median filtering properties of removing outlier error while respecting edges/structures. This reveals that the previously overlooked refinement can be at least as crucial as aggregation. We also develop the first constant time algorithm for the previously time-consuming weighted median filter. This makes the simple combination ``box aggregation + weighted median'' an attractive solution in practice for both speed and accuracy. As a byproduct, the fast weighted median filtering unleashes its potential in other applications that were hampered by high complexities. We show its superiority in various applications such as depth up sampling, clip-art JPEG artifact removal, and image stylization.read more
Citations
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References
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A fast approximation of the bilateral filter using a signal processing approach
Sylvain Paris,Frédo Durand +1 more
TL;DR: A new signal-processing analysis of the bilateral filter is proposed, which complements the recent studies that analyzed it as a PDE or as a robust statistics estimator and allows for a novel bilateral filtering acceleration using a downsampling in space and intensity.
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A pixel dissimilarity measure that is insensitive to image sampling
Stan Birchfield,Carlo Tomasi +1 more
TL;DR: Experiments show that the proposed measure of dissimilarity uses the linearly interpolated intensity functions surrounding the pixels alleviates the problem of sampling with little additional computational overhead.
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Cross-Based Local Stereo Matching Using Orthogonal Integral Images
TL;DR: An area-based local stereo matching algorithm for accurate disparity estimation across all image regions, and is among the best performing local stereo methods according to the benchmark Middlebury stereo evaluation.
Proceedings ArticleDOI
A non-local cost aggregation method for stereo matching
TL;DR: The cost aggregation problem is re-examined and a non-local solution is proposed which outperforms all local cost aggregation methods on the standard (Middlebury) benchmark and has great advantage in extremely low computational complexity.
Proceedings ArticleDOI
Constant time O(1) bilateral filtering
TL;DR: Three novel methods that enable bilateral filtering in constant time O(1) without sampling are presented and it is shown that Gaussian range and arbitrary spatial bilateral filters can be expressed by Taylor series as linear filter decompositions without any noticeable degradation of filter response.