Real-time O(1) bilateral filtering

By Qingxiong Yang, Kar-Han Tan, Narendra Ahuja. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009

433 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b2d5b1d4a196e8c5000bfc6d3b172dbdfc273755

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In Computer Vision and Pattern Recognition

Citations in the corpus

Fast Semantic Image Segmentation with High Order Context and Guided Filtering (2016, methodology): “Though the brute force implementation of bilateral filter is very slow, there are many speedy versions using the techniques of down-sampling [19] or quantization [20].”
Rolling Guidance Filter (2014, methodology): “[34] 5 16 Covariance M2 [14] 198 614 Ours with BF [33] 0.”
Constant Time Weighted Median Filtering for Stereo Matching and Beyond (2013, background): “The weighted median filter inherits some desired properties from both median filtering and edge-aware averaging filtering (the guided filter).”
Guided Image Filtering (2010, background): “Under this viewpoint, both [34] and [18] sample the signal along the range domain but do not reconstruct it.”
PCA-Based Edge-Preserving Features for Hyperspectral Image Classification (2017, background): “Although a number of techniques have been proposed to accelerate anisotropic diffusion or bilateral filtering, most of these methods can only be applied to grayscale images or rely on approximate operations [42]–[44].”
A non-local cost aggregation method for stereo matching (2012, methodology): “A number of approximation methods have been developed to accelerate the bilateral filter, including Paris and Durand’s fast bilateral filter [12], Porikli’s O(1) bilateral filter [17] and Yang’s real-time bilateral filters [25, 26].”
Medical image processing on the GPU - Past, present and future (2013, methodology): “Similar work has been presented by Yang et al. (2009), who developed a parallel implementation of O(1) bilateral filtering (for which the processing time is invariant to the filter kernel size).”
Fast Global Image Smoothing Based on Weighted Least Squares (2014, positive): “Specifically, we can define the following energy function with 0 γ 2: Jγ(u) = ∑ p (up − fp)2 + λ ∑ q∈N4(p) wp,q(g)|up − uq|γ .”
Hyperspectral Anomaly Detection With Attribute and Edge-Preserving Filters (2017, neutral): “dle grayscale images [32]–[34].”