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《Journal of Jiangxi Normal University (Natural Sciences Edition)》 2005-01
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Locally Adaptive Bivariate Shrinkage Wavelet Denoising Based on Inter- and Intra-scale Dependency

DENG Cheng-zhi~1, WANG Sheng-qian~1, LIU Zhu-hua~2, WANG Zhong-hua~1, ZOU Dao-wen~2 (1.Key Laboratory of Photoelectron & Communication, Jiangxi Normal University, Nanchang, Jiangxi 330027; 2.Department of applied physics, Jiangxi Science & Technology Teacher College, Nanchang, Jiangxi, 330013))  
There are strong dependencies between wavelet coefficients of images.In this paper,considering inter- scale dependency,we introduced a bivariate probability distribution model. The corresponding nonlinear threshold functions (bivariate shrinkage function) are derived from the model using the Bayesian estimation theory. And then, based on the intra-scale dependency we present a locally adaptive denoising algorithm using the bivariate shrinkage function. We compare this algorithm with the Donoho's hard thresholding, BayesShrink and HMT. Experimental results show this algorithm can receive better denoising results.
【Fund】: 国家自然科学基金资助项目(60462003);; 江西省自然科学基金资助项目(0412008).
【CateGory Index】: TN911.7
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