Please verify that (1) all pages are present, (2) all figures are acceptable, (3) all fonts and special characters are correct, and (4) all text and figures fit within the
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1628
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A theory for multiresolution signal decomposition: the wavelet representation
– Mallat
- 1989
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986
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Embedded image coding using zerotrees of wavelet coefficients
– Shapiro
- 1993
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460
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Emergence of simple-cell receptive field properties by learning a sparse code for natural images
– Olshausen, Field
- 1996
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340
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Ideal spatial adaptation via wavelet shrinkage
– Donoho, Johnstone
- 1994
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316
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Shiftable multiscale transforms
– Simoncelli, Freeman, et al.
- 1992
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210
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Wavelet-based statistical signal processing using hidden Markov models
– Crouse, Nowak, et al.
- 1998
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160
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Translation-invariant de-noising
– Coifman, Donoho
- 1995
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129
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Image compression via joint statistical characterization in the wavelet domain
– Buccigrossi, Simoncelli
- 1999
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126
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The curvelet transform for image denoising
– Starck, Candès, et al.
- 2002
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126
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The steerable pyramid: A flexible architecture for multi-scale derivative computation
– Simoncelli, Freeman
- 1995
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125
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Noise removal via Bayesian wavelet coring
– Simoncelli, Adelson
- 1996
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106
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Image denoising using scale mixtures of Gaussians in the wavelet domain
– Portilla, Strela, et al.
- 2003
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106
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Complex wavelets for shift invariant analysis and filtering of signals
– Kingsbury
- 2001
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97
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Spatially adaptive wavelet thresholding with context modeling for image denoising
– Chang, Yu, et al.
- 1998
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93
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Statistical Models for Images: Compression, Restoration and Synthesis
– Simoncelli
- 1997
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85
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Low-Complexity Image Denoising Based on Statistical Modeling of Wavelet Coefficients
– Mihcak, Kozintsev, et al.
- 1999
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84
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Digital image enhancement and noise filtering by use of local statistics
– LEE
- 1980
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68
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Scale mixtures of gaussians and the statistics of natural images
– Wainwright, Simoncelli
- 2000
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61
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Scale mixtures of normal distributions
– Andrews, Mallows
- 1974
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60
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Bayesian denoising of visual images in the wavelet domain
– Simoncelli
- 1999
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57
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Random cascades on wavelet trees and their use in analyzing and modeling natural images
– Wainwright, Simoncelli, et al.
- 2001
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48
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Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency
– Sendur, Selesnick
- 2002
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28
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Translation-invariant de-noising,” in Wavelets and
– Coifman, Donoho
- 1995
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28
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A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising
– Pizurica, Philips, et al.
- 2002
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27
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Wavelet-based image estimation: an empirical Bayes approach using Jeffreys’ noninformative prior
– Figueiredo, Nowak
- 2001
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27
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Wavelet-based image denoising using a Markov random field a priori model
– Malfait, Roose
- 1997
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22
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Adaptive Wiener denoising using a Gaussian scale mixture model in the wavelet domain
– Portilla, Strela, et al.
- 2001
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22
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Spatially adaptive image denoising under overcomplete expansion
– Li, Orchard
- 2000
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22
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Image denoising using a local Gaussian scale mixture model in the wavelet domain
– Strela, Portilla, et al.
- 2000
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19
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Image denoising using gaussian scale mixtures in the wavelet domain
– Portilla, Strela, et al.
- 2002
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10
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Wavelet image coding based on a new generalized gaussian mixture model
– LoPresto, Ramchandran, et al.
- 1997
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10
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Denoising via block Wiener filtering in wavelet domain
– Strela
- 2000
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6
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Image denoising using scale mixtures
– Portilla, Strela, et al.
- 2003
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6
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Two-level adaptive denoising using Gaussian scale mixtures in overcomplete oriented pyramids
– Guerrero-Colon, Portilla
- 2005
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5
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Bayesian wavelet-based image estimation using noninformative priors
– Figueiredo, Nowak
- 1999
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5
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Full blind denoising through noise covariance estimation using Gaussian scale mixtures in the wavelet domain
– Portilla
- 2004
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5
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Image denoising using a tight frame
– Shen, Papadakis, et al.
- 2006
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5
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Hierarchical image probability (HIP) models
– Spence, Parra
- 2000
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3
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Hirarchical image probability (HIP) model
– Spence, Parra
- 2000
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2
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non-white noise removal in images using gaussian scale mixtures in the wavelet domain
– Portilla, “Blind
- 2004
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1
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Prior information and subjective probability,” in Statistical Decission Theory and Bayesian Analysis, Springer Series in Statistics
– Berger
- 1990
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1
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Unsupervised Learning and Clustering. Wiley Interscience, 2001. ∂C(Cw) ∂Cw = = M� m=1 ∂ ∂Cw M� � m=1 APPENDIX A. �� � log py|z(ym|z; Cw)pz(z)dz = z m=1 z pz(z)py|z(ym|z; Cw) ∂ log py|z(ym|z;Cw) dz ∂Cw � z0 p y|z(ym|z0; Cw)pz(z0)dz0 ∂ log py|z(ym|z; Cw) =−
– Duda, Hart, et al.
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