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Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency
Abstract
Most simple nonlinear thresholding rules for wavelet-based denoising assume that the wavelet coefficients are independent. However, wavelet coefficients of natural images have significant dependencies. We only consider the dependencies between the coefficients and their parents in detail. For this purpose, new non-Gaussian bivariate distributions are proposed, and corresponding nonlinear threshold functions (shrinkage functions) are derived from the models using Bayesian estimation theory. The new shrinkage functions do not assume the independence of wavelet coefficients. We show three image denoising examples in order to show the performance of these new bivariate shrinkage rules. In the second example, a simple subband-dependent data-driven image denoising system is described and compared with effective data-driven techniques in the literature, namely VisuShrink, SureShrink, BayesShrink, and hidden Markov models. In the third example, the same idea is applied to the dual-tree complex wavelet coefficients.
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Cites
Natural Image Statistics and Neural Representation
Simoncelli, E. P., & Olshausen, B. A. (2001) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.24.1.1193 · Cited by 2,460
Cited by
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
Nonlocally Centralized Sparse Representation for Image Restoration
Dong, W., Zhang, L., Shi, G., & Li, X. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2235847 · Cited by 1,505
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
A study of the robustness of PRNU-based camera identification
Rosenfeld, K., & Sencar, H. T. (2009) · SPIE Proceedings · doi:10.1117/12.814705 · Cited by 88
Metadata
- Authors
- Sendur, L., & Selesnick, I. W.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2002-11
- Volume
- 50
- Issue
- 11
- Pages
- 2744-2756
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2170885533
- Cited by
- 1,014
- References
- 1
- FWCI
- 25.09
- Type
- journal article
- Language
- en
- Open access
- closed
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