TY - JOUR
T1 - Wavelet-based Bayesian image estimation
T2 - From marginal and bivariate prior models to multivariate prior models
AU - Tan, Shan
AU - Jiao, Licheng
AU - Kakadiaris, Ioannis A.
N1 - Funding Information:
Manuscript received September 26, 2006; revised November 26, 2007. This work was supported in part by the National Natural Science Foundation of China under Grant 60672126, in part by the National Basic Research Program (973 Program) of China under Grant 2006CB705700, and in part by the Science and Technology innovation engineering important project in university of China under Grant 706053. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Srdjan Stankovic.
PY - 2008/4
Y1 - 2008/4
N2 - Prior models play an important role in the wavelet-based Bayesian image estimation problem. Although it is well known that a residual dependency structure always remains among natural image wavelet coefficients, only few multivariate prior models with a closed parametric form are available in the literature. In this paper, we develop new multivariate prior models that not only match well with the observed statistics of the wavelet coefficients of natural images, but also have a simple parametric form. These prior models are very effective for Bayesian image estimation and lead to an improved estimation performance over related earlier techniques.
AB - Prior models play an important role in the wavelet-based Bayesian image estimation problem. Although it is well known that a residual dependency structure always remains among natural image wavelet coefficients, only few multivariate prior models with a closed parametric form are available in the literature. In this paper, we develop new multivariate prior models that not only match well with the observed statistics of the wavelet coefficients of natural images, but also have a simple parametric form. These prior models are very effective for Bayesian image estimation and lead to an improved estimation performance over related earlier techniques.
KW - Elliptically contoured distribution family
KW - Image estimation
KW - Multivariate model
KW - Natural image statistics
UR - http://www.scopus.com/inward/record.url?scp=41849092337&partnerID=8YFLogxK
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U2 - 10.1109/TIP.2008.918018
DO - 10.1109/TIP.2008.918018
M3 - Article
C2 - 18390356
AN - SCOPUS:41849092337
VL - 17
SP - 469
EP - 481
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
SN - 1057-7149
IS - 4
ER -