基于Curvelet稀疏表示的图像盲分离初始化

Journal of Applied Sciences ›› 2009, Vol. 27 ›› Issue (2): 161-166.

• Signal and Information Processing • Previous Articles Next Articles

Initialization Algorithm for Blind Image Separation Based on Curvelet Sparse Representation

1. School of Communication and Information Engineering, Shanghai University, Shanghai 200072 China
2. Key Laboratory of Special Optical Fibers and Optical Access Network, Shanghai University, Shanghai 200072, China

Received:2008-07-22 Revised:2008-10-18 Online:2009-04-01 Published:2009-04-01

Abstract

Abstract:

A new initialization algorithm for blind separation of images is proposed based on curvelet sparse representation. A mixed matrix can be estimated by estimating the center of received signals. This method can
improve convergence and effectively avoid falling into local minima. Simulation results show that the proposed
algorithm can achieve better performance for blind source separation of images.

Key words: blind source separation, sparse representation, Curvelet transform, initialization

CLC Number:

TP391

WANG Jun-hua1, FANG Yong1;2. Initialization Algorithm for Blind Image Separation Based on Curvelet Sparse Representation[J]. Journal of Applied Sciences, 2009, 27(2): 161-166.

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Initialization Algorithm for Blind Image Separation Based on Curvelet Sparse Representation

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