The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Multiresolution Bilateral Filtering for Image Denoising
Open accessAbstract
The bilateral filter is a nonlinear filter that does spatial averaging without smoothing edges; it has shown to be an effective image denoising technique. An important issue with the application of the bilateral filter is the selection of the filter parameters, which affect the results significantly. There are two main contributions of this paper. The first contribution is an empirical study of the optimal bilateral filter parameter selection in image denoising applications. The second contribution is an extension of the bilateral filter: multiresolution bilateral filter, where bilateral filtering is applied to the approximation (low-frequency) subbands of a signal decomposed using a wavelet filter bank. The multiresolution bilateral filter is combined with wavelet thresholding to form a new image denoising framework, which turns out to be very effective in eliminating noise in real noisy images. Experimental results with both simulated and real data are provided.
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Cites
Advances in Neural Information Processing Systems 19
(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 15,539
Image denoising by sparse 3-D transform-domain collaborative filtering
Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.901238 · Cited by 9,283
Ideal spatial adaptation by wavelet shrinkage
Donoho, D. L., & Johnstone, I. M. (1994) · Biometrika · doi:10.1093/biomet/81.3.425 · Cited by 8,760
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 7×
“TABLE I PSNR COMPARISON OF THE BAYESSHRINK METHOD [3] , THE BILATERAL FILTER [16], SEQUENTIAL APPLICATION OF THE BAYESSHRINK [3] AND THE BILATERAL FILTER [16] METHODS, NEW SURE THRESHOLDING [8], 3-”
InfluentialImage Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
Elad, M., & Aharon, M. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881969 · Cited by 5,321
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
Adaptive wavelet thresholding for image denoising and compression
Chang, S. G., Yu, B., & Vetterli, M. (2000) · IEEE Transactions on Image Processing · doi:10.1109/83.862633 · Cited by 2,936
Image denoising using scale mixtures of gaussians in the wavelet domain
Portilla, J., Strela, V., Wainwright, M. J., & Simoncelli, E. P. (2003) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.818640 · Cited by 2,255
Wavelet Shrinkage: Asymptopia?
Donoho, D. L., Johnstone, I. M., Kerkyacharian, G., & Picard, D. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02032.x · Cited by 1,749
Sparse Representation for Color Image Restoration
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Cited by
Image fusion based on pixel significance using cross bilateral filter
Shreyamsha Kumar, B. K. (2015) · Signal, Image and Video Processing · doi:10.1007/s11760-013-0556-9 · Cited by 567
Metadata
- Authors
- Zhang, M., & Gunturk, B. K.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2008-12
- Volume
- 17
- Issue
- 12
- Pages
- 2324-2333
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2100925004
- Cited by
- 512
- References
- 36
- FWCI
- 27.21
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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