The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
On the origin of the bilateral filter and ways to improve it
Abstract
Additive noise removal from a given signal is an important problem in signal processing. Among the most appealing aspects of this field are the ability to refer it to a well-established theory, and the fact that the proposed algorithms in this field are efficient and practical. Adaptive methods based on anisotropic diffusion (AD), weighted least squares (WLS), and robust estimation (RE) were proposed as iterative locally adaptive machines for noise removal. Tomasi and Manduchi (see Proc. 6th Int. Conf. Computer Vision, New Delhi, India, p.839-46, 1998) proposed an alternative noniterative bilateral filter for removing noise from images. This filter was shown to give similar and possibly better results to the ones obtained by iterative approaches. However, the bilateral filter was proposed as an intuitive tool without theoretical connection to the classical approaches. We propose such a bridge, and show that the bilateral filter also emerges from the Bayesian approach, as a single iteration of some well-known iterative algorithm. Based on this observation, we also show how the bilateral filter can be improved and extended to treat more general reconstruction problems.
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Citations
Cites
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 9×
“Tomasi and Manduchi proposed the bilateral filter in 1998 [14] as an appealing algorithm for noise removal from images.”
BackgroundMethodResultInfluential
Cited by
He, K., Sun, J., & Tang, X. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.213 · Cited by 5,546
A universal noise removal algorithm with an impulse detector
Garnett, R., Huegerich, T., Chui, C., & He, W. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.857261 · Cited by 615
Bilateral Filtering: Theory and Applications
Paris, S., Kornprobst, P., Tumblin, J., & Durand, F. (2009) · Foundations and Trends in Computer Graphics and Vision · doi:10.1561/0600000020 · Cited by 586
Adaptive Bilateral Filter for Sharpness Enhancement and Noise Removal
Zhang, B., & Allebach, J. P. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2008.919949 · Cited by 403
Automatic breast ultrasound image segmentation: a survey
Xian, M., Zhang, Y., Cheng, H. D., Xu, F., Zhang, B., & Ding, J. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.02.012 · Cited by 284
“, mean filter, Gaussian low-pass filter, speckle reducing anisotropic diffusion (SRAD) [148], nonlinear coherence diffusion (NCD) [149], sticks filter [60], bilateral filter [150, 151], fractional subpixel diffusion [157], etc.”
Background
Metadata
- Authors
- Elad, M.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2002-10
- Volume
- 11
- Issue
- 10
- Pages
- 1141-1151
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2109991658
- Cited by
- 791
- References
- 1
- FWCI
- 14.23
- Type
- journal article
- Language
- en
- Open access
- closed
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- Elad, M. (2002). On the origin of the bilateral filter and ways to improve it. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2002.801126
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
