首页|应用于盲图像分离的基于Contourlet变换的图像稀疏分量分析算法

应用于盲图像分离的基于Contourlet变换的图像稀疏分量分析算法

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A contourlet-transform based sparse ICA algorithm for blind image separation
A contourlet-transform (CT) based sparse independent component analysis for blind image separation is proposed. The images are first decomposed into sets of local features with various degrees of sparsity, and then the intrinsic property is used to select the best (sparsest) subsets of features for further separation. Based on sparse description of the contourlet-transform, the proposed approach is able to yield better performance, including faster convergence and the certain order for the separated signals. Simulation results confirm the validity of the proposed method.

blind source separation, sparse independent component analysis, contourlet-transform (CT).

刘盛鹏、方勇

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School of Communication and Information Engineering, Shanghai University, Shanghai 200072, P. R. China

blind source separation, sparse independent component analysis, contourlet-transform (CT).

国家自然科学基金Shanghai Excellent Academic Leader ProjectShanghai Leading Academic Discipline Project

6047210305XP14027T0102

2007

上海大学学报(英文版)
上海大学

上海大学学报(英文版)

影响因子：0.196
ISSN：1007-6417
年,卷(期)：2007.11(5)
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