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Independent Component Analysis in Image Denoising
, 1999
"... Independent component analysis (ICA) is a statistical technique which attempts to find a representation of observed data such that the components are as independent as possible. This technique has shown great promise in feature extraction, essentially finding the building blocks of any given data. I ..."
Abstract

Cited by 7 (0 self)
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Independent component analysis (ICA) is a statistical technique which attempts to find a representation of observed data such that the components are as independent as possible. This technique has shown great promise in feature extraction, essentially finding the building blocks of any given data. In particular, when applied to image data ICA gives a representation which identifies the contours in the image; these can be considered the primary structure in the data.
Hidden markov models for waveletbased signal processing
 in Proc. 30th Asilomar Conf
, 1996
"... Current waveletbased statistical signal and image pmcessing techniques such as shrinkage andfiltering treat the wavelet coefficients as though they were statistically independent. This assumption is unrealistic; considering the statistical dependencies between wavelet coeficients can yield substa ..."
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Cited by 5 (4 self)
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Current waveletbased statistical signal and image pmcessing techniques such as shrinkage andfiltering treat the wavelet coefficients as though they were statistically independent. This assumption is unrealistic; considering the statistical dependencies between wavelet coeficients can yield substantial pe$onnance improvements. In this papel; we develop a new framework for waveletbased signal pmcessing that employs hidden Markov models to characterize the dependencies between wavelet coeficients. To illustrate the power of the new framework, we derive a new signal denoising algorithm that outperfoms current scalar shrinkage techniques. 1