The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Bivariate shrinkage with local variance estimation
Abstract
The performance of image-denoising algorithms using wavelet transforms can be improved significantly by taking into account the statistical dependencies among wavelet coefficients as demonstrated by several algorithms presented in the literature. In two earlier papers by the authors, a simple bivariate shrinkage rule is described using a coefficient and its parent. The performance can also be improved using simple models by estimating model parameters in a local neighborhood. This letter presents a locally adaptive denoising algorithm using the bivariate shrinkage function. The algorithm is illustrated using both the orthogonal and dual tree complex wavelet transforms. Some comparisons with the best available results are given in order to illustrate the effectiveness of the proposed algorithm.
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Citations
Cites
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
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
The curvelet transform for image denoising
Starck, J.-L., Candes, E. J., & Donoho, D. L. (2002) · IEEE Transactions on Image Processing · doi:10.1109/tip.2002.1014998 · Cited by 2,253
Complex Wavelets for Shift Invariant Analysis and Filtering of Signals
Kingsbury, N. (2001) · Applied and Computational Harmonic Analysis · doi:10.1006/acha.2000.0343 · Cited by 1,675
Wavelet-based statistical signal processing using hidden Markov models
Crouse, M. S., Nowak, R. D., & Baraniuk, R. G. (1998) · IEEE Transactions on Signal Processing · doi:10.1109/78.668544 · Cited by 1,643
Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency
Sendur, L., & Selesnick, I. W. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2002.804091 · Cited by 1,014
Self-citationSpatially adaptive wavelet thresholding with context modeling for image denoising
Chang, S. G., Yu, B., & Vetterli, M. (2000) · IEEE Transactions on Image Processing · doi:10.1109/83.862630 · Cited by 893
Image processing with complex wavelets
Kingsbury, N. (1999) · Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.1999.0447 · Cited by 805
Low-complexity image denoising based on statistical modeling of wavelet coefficients
Kivanc Mihcak, M., Kozintsev, I., Ramchandran, K., & Moulin, P. (1999) · IEEE Signal Processing Letters · doi:10.1109/97.803428 · Cited by 780
Scale Mixtures of Gaussians and the Statistics of Natural Images
Wainwright, M. J., & Simoncelli, E. P. (1999) · Cited by 450
Cited by
A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding
Luisier, F., Blu, T., & Unser, M. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.891064 · Cited by 623
Metadata
- Authors
- Sendur, L., & Selesnick, I. W.
- Venue
- IEEE Signal Processing Letters
- Published
- 2002-12
- Volume
- 9
- Issue
- 12
- Pages
- 438-441
- ISSN
- 1070-9908 (ISSN-L)
- 1558-2361
- OpenAlex
- W2152047336
- Cited by
- 579
- References
- 11
- FWCI
- 13.48
- Type
- journal article
- Language
- en
- Open access
- closed
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