@inproceedings {533,
	title = {Modeling the correlation structure of images in the wavelet domain},
	booktitle = {14th Canadian Conference on Electrical and Computer Engineering},
	year = {2001},
	address = {Toronto},
	abstract = {<p>In this paper we investigate the correlation structure of the wavelet coefficients corresponding to random fields. The context of this work is the study of Bayesian approaches to wavelet shrinkage for the purposes of image denoising. This paper concentrates on both within-scale and across-scale statistical dependencies for a variety of wavelets and random fields, with examples provided for both 1-D and 2-D signals. The results show the whitening effect of the wavelet transform to be quite clear-even for particular highly correlated spatial processes the within-scale correlation decays exponentially fast, however the correlation between scales is surprisingly substantial, even for separations several scales apart. Our goal, initiated in this paper, is the development of an efficient random field model, describing these statistical correlations, and the demonstration of its effectiveness in the context of Bayesian wavelet shrinkage for signal and image denoising</p>
},
	keywords = {1-D signals, 2-D signals, across-scale statistical dependencies, Bayes methods, Bayesian approaches, correlation structure, correlation theory, Image Denoising, image enhancement, random field model, random fields, wavelet coefficients, wavelet domain, wavelet shrinkage, wavelet transforms, white noise, whitening effect, within-scale statistical dependencies},
	doi = {http://dx.doi.org/10.1109/CCECE.2001.933599},
	author = {Z Azimifar and P Fieguth and E Jernigan}
}
@inproceedings {568,
	title = {Wavelet shrinkage with correlated wavelet coefficients},
	booktitle = {International Conference on Image Processing},
	year = {2001},
	address = {Greece},
	abstract = {<p>This paper investigates the statistical characterization of multiscale wavelet coefficients corresponding to random signals and images. Virtually all approaches to wavelet shrinkage model the wavelet coefficients as independent; we challenge that assumption and demonstrate several cases where substantial correlations may be present in the wavelet domain. In particular, the correlation between scales can be surprisingly substantial, even for pixels separated by several scales. Our goal is to develop an efficient random field model describing these statistical correlations, and demonstrate its effectiveness in the context of Bayesian wavelet shrinkage for signal and image denoising</p>
},
	keywords = {Bayes methods, Bayesian wavelet shrinkage, correlated wavelet coefficients, Image Denoising, image resolution, multiscale wavelet coefficients, random field model, signal denoising, smoothing methods, statistical analysis, statistical correlations, wavelet transforms},
	doi = {http://dx.doi.org/10.1109/ICIP.2001.958076},
	author = {Z Azimifar and P Fieguth and E Jernigan}
}
