Signal estimation using wavelet-Markov models
نویسندگان
چکیده
Current wavelet-based statistical signal and image processing techniques such as shrinkage and filtering treat the wavelet coefficients as though they were statistically independent. This assumption is unrealistic; considering the statistical dependencies between wavelet coefficients can yield substantial performance improvements. In this paper, we develop a new framework for wavelet-based signal processing that employs hidden Markov models to characterize the dependencies between wavelet coefficients. To illustrate the power of the new framework, we derive a new algorithm for signal estimation in nonGaussian noise.
منابع مشابه
Simplified wavelet-domain hidden Markov models using contexts
Wavelet-domain Hidden Markov Models (HMMs) are a potent new tool for modeling the statistical properties of wavelet transforms. In addition to characterizing the statistics of individual wavelet coefficients, HMMs capture the salient interactions between wavelet coefficients. However, as we model an increasing number of wavelet coefficient interactions, HMM-based signal processing becomes incre...
متن کاملWavelet-based statistical signal processing using hidden Markov models
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. In this paper, we develop a new framework for statistical signal processing based on wavelet-domain hidden Markov models (HMM’s) that concisely models the statistical dependen...
متن کاملCan Wavelet Denoising Improve Motor Unit Potential Template Estimation?
Background: Electromyographic (EMG) signals obtained from a contracted muscle contain valuable information on its activity and health status. Much of this information lies in motor unit potentials (MUPs) of its motor units (MUs), collected during the muscle contraction. Hence, accurate estimation of a MUP template for each MU is crucial. Objective: To investigate the possibility of improv...
متن کاملContextual Hidden Markov Models forWavelet - domain Signal
Wavelet-domain Hidden Markov Models (HMMs) provide a powerful new approach for statistical model-ing and processing of wavelet coeecients. In addition to characterizing the statistics of individual wavelet coeecients, HMMs capture some of the key interactions between wavelet coeecients. However, as HMMs model an increasing number of wavelet coeecient interactions , HMM-based signal processing b...
متن کاملBivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency
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. In this paper, we will only consider the dependencies between the coefficients and their parents in detail. For this purpose, new non-Gaussian bivariate distributions are proposed, and correspond...
متن کامل