The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Wavelet-based statistical signal processing using hidden Markov models
Abstract
Wavelet-based statistical signal processing techniques such as denoising and detection typically model the wavelet coefficients as independent or jointly Gaussian. These models are unrealistic for many real-world signals. We develop a new framework for statistical signal processing based on wavelet-domain hidden Markov models (HMMs) that concisely models the statistical dependencies and non-Gaussian statistics encountered in real-world signals. Wavelet-domain HMMs are designed with the intrinsic properties of the wavelet transform in mind and provide powerful, yet tractable, probabilistic signal models. Efficient expectation maximization algorithms are developed for fitting the HMMs to observational signal data. The new framework is suitable for a wide range of applications, including signal estimation, detection, classification, prediction, and even synthesis. To demonstrate the utility of wavelet-domain HMMs, we develop novel algorithms for signal denoising, classification, and detection.
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Cites
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Adapting to Unknown Smoothness via Wavelet Shrinkage
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Maximum likelihood estimation from incomplete data via the EM algorithm
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Maximum Likelihood from Incomplete Data via the EM Algorithm
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Modern Image Quality Assessment
Wang, Z., & Bovik, A. C. (2006) · Synthesis Lectures on Image, Video, and Multimedia Processing · doi:10.1007/978-3-031-02238-8 · Cited by 967
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
Wavelet Thresholding via A Bayesian Approach
Abramovich, F., Sapatinas, T., & Silverman, B. W. (1998) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00151 · Cited by 548
Automatic Estimation and Removal of Noise from a Single Image
Liu, C., Szeliski, R., Kang, S. B., Zitnick, C. L., & Freeman, W. T. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1176 · Cited by 486
Reduced-reference image quality assessment using a wavelet-domain natural image statistic model
Wang, Z., & Simoncelli, E. P. (2005) · SPIE Proceedings · doi:10.1117/12.597306 · Cited by 438
Metadata
- Authors
- Crouse, M. S., Nowak, R. D., & Baraniuk, R. G.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 1998-04
- Volume
- 46
- Issue
- 4
- Pages
- 886-902
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2134929491
- Cited by
- 1,643
- References
- 29
- FWCI
- 44.40
- Type
- journal article
- Language
- en
- Open access
- closed
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- Crouse, M. S., Nowak, R. D., & Baraniuk, R. G. (1998). Wavelet-based statistical signal processing using hidden Markov models. IEEE Transactions on Signal Processing. https://doi.org/10.1109/78.668544
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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).
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