The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
Bilateral Filtering: Theory and Applications
Abstract
The bilateral filter is a non-linear technique that can blur an image while respecting strong edges. Its ability to decompose an image into different scales without causing haloes after modification has made it ubiquitous in computational photography applications such as tone mapping, style transfer, relighting, and denoising. This text provides a graphical, intuitive introduction to bilateral filtering, a practical guide for efficient implementation and an overview of its numerous applications, as well as mathematical analysis.
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Citations
Cites
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
A Wavelet Tour of Signal Processing
(1999) · Elsevier eBooks · doi:10.1016/b978-0-12-466606-1.x5000-4 · Cited by 16,227
Scale-space and edge detection using anisotropic diffusion
Perona, P., & Malik, J. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.56205 · Cited by 12,119
(1981) · Cited by 11,963
Bilateral Filtering for Gray and Color Images
Tomasi, C., & Manduchi, R. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710815 · Cited by 8,177 · cited 3×
“With the former choice of wσs , we get the SUSAN filter [59] or the bilateral filter [63] (see Section 2).”
BackgroundMethodHuber, P. J. (1981) · Wiley Series in Probability and Statistics · doi:10.1002/0471725250 · Cited by 7,066
Computational modelling of visual attention
Itti, L., & Koch, C. (2001) · Nature Reviews Neuroscience · doi:10.1038/35058500 · Cited by 4,733
Land, E. H., & McCann, J. J. (1971) · Journal of the Optical Society of America · doi:10.1364/josa.61.000001 · Cited by 4,095
Robust Statistics: The Approach Based on Influence Functions
Ruppert, D. (1987) · Technometrics · doi:10.2307/1269782 · Cited by 3,880
SUSAN—a new approach to low level image processing
Smith, S. M., & Brady, J. M. (1997) · International Journal of Computer Vision · doi:10.1023/a:1007963824710 · Cited by 3,333
Cited by
Pixel-Adaptive Convolutional Neural Networks
Su, H., Jampani, V., Sun, D., Gallo, O., Learned-Miller, E., & Kautz, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01142 · Cited by 348
Pedestrian detection under weather conditions using conditional generative adversarial network
Razzok, M., Badri, A., Mourabit, I. E., Ruichek, Y., & Sahel, A. (2023) · IAES International Journal of Artificial Intelligence (IJ-AI) · doi:10.11591/ijai.v12.i4.pp1557-1568 · Cited by 12
Metadata
- Authors
- Paris, S., Kornprobst, P., Tumblin, J., & Durand, F.
- Venue
- Foundations and Trends in Computer Graphics and Vision
- Publisher
- Emerald
- Published
- 2009-08-28
- Volume
- 4
- Issue
- 1
- Pages
- 1-73
- ISSN
- 1572-2740 (ISSN-L)
- 1572-2759
- OpenAlex
- W1995194116
- Cited by
- 586
- References
- 72
- FWCI
- 23.09
- Type
- journal article
- Language
- en
- Open access
- closed
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