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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Joint Graph-Based Depth Refinement and Normal Estimation
TL;DR: A novel depth refinement framework that aims at recovering the underlying piece-wise planarity of those inverse depth maps associated to piece- wise planar scenes, and leads to a significant improvement in depth refinement, both visually and numerically, with respect to state-of-the-art algorithms on the Middlebury, KITTI and ETH3D multi-view datasets.
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Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction
TL;DR: This paper presents a detail-preserving and content-aware variational (DCV) MVS method, which reconstructs the 3D surface by alternating between reprojection error minimization and mesh denoising, and proposes a novel inter-image similarity measure which is effective to preserve fine-scale details of the reconstructed surface.
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Image Structure Retrieval via $L_0$ Minimization
TL;DR: This work presents a method to retrieve salient structure in a textured image using an minimization of a modified form of the relative total variation metric, which outperforms state-of-art methods in texture removal as well as scale-space filtering.
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Improved Cost Computation and Adaptive Shape Guided Filter for Local Stereo Matching of Low Texture Stereo Images
TL;DR: An efficient and effective matching cost measurement and an adaptive shape guided filter-based matching cost aggregation method to improve the stereo matching performance for large textureless regions.
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Classification of magnetic resonance images for brain tumour detection
TL;DR: This study presents an automatic lesion recognition method in the MRI followed by classification, which reports 94.5 and 91.76%, average accuracy of segmentation and classification, respectively.
References
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TL;DR: The guided filter is a novel explicit image filter derived from a local linear model that can be used as an edge-preserving smoothing operator like the popular bilateral filter, but it has better behaviors near edges.
Journal ArticleDOI
Fast bilateral filtering for the display of high-dynamic-range images
DurandFrédo,DorseyJulie +1 more
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