Mendeley
TY  _  JOUR
ID  -  13940904101437511
TI  -  Using Minimum Mean Squared Error Estimator for Quality Improvement of Abdominal Computerized Tomography Images Based on a Bivariate Laplacian Mixture Model for Complex Wavelet Coefficient
JO  -  Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran
JA  -  ES
LA  -  fa
SN  -  16823745
AU  -  Rabbani H.
AU  -  Vafadust M.
AD  -  دانشگاه علوم پزشكي اصفهان
AD  -  دانشگاه صنعتي اميركبير
Y1  -  2009
PY  -  2009
VL  -  2
IS  -  
SP  -  162
EP  -  169
KW  -  Discrete complex wavelet transformminimum mean squared errorbivariate modelsmixture models
DO  -  
N2  -  One of the important subjects in the wavelet-based image denoising based on the Bayes theorem is choosing the appropriate density function for modeling the wavelet coefficients.  The interscale dependency between parent and child coefficients is one of the statistical properties of wavelets. So, in the recent years instead of univariate distribution, bivariate density functions have been suggested by the researchers and in this paper we use a mixture of bivariate Laplacian densities for this reason. Using this distribution we are able to model both heavy-tailed property and interscale dependency of wavelets. Using the mentioned density function for a minimum mean squared error estimator, we obtain a new shrinkage function for denoising. Applying this function to each subband of discrete complex wavelet transform of abdominal computerized tomography images, we will be able to improve the quality of these images better than some reported methods.
UR  - rimag.ir/en/Article/27947
L1  - rimag.ir/en/Article/Download/27947
ER  -  
