Blind Image Separation Based on Wavelet Transformation and Sparse Component Analysis

doi:10.13190/jbupt.201002.58.yuxch

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2010, Vol. 33 ›› Issue (2): 58-63.doi: 10.13190/jbupt.201002.58.yuxch

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Blind Image Separation Based on Wavelet Transformation and Sparse Component Analysis

YU Xian-chuan, CAO Ting-ting, HU Dan, ZHANG Li-bao, DAI Sha

(Department of Information Science and Technolgy, Beijing Normal University, Beijing 100875, China)

Received:2009-06-13 Revised:2009-11-30 Online:2010-04-28 Published:2010-04-28

Abstract

Abstract:

Blind source separation is one of the hot spots of signal processing domain. Considering that mixed images cant be separated with sparse component analysis (SCA) model for image doesnt always satisfy sparse conditions, a method based on wavelet transformation and SCA (WTSCA) is proposed to extract source images. WT is used to transform mixed images to frequency domain. SCA is used to estimate mixing matrix, and to reconstruct source images. The experiments manifest that WTSCA can accurately and effectively extract sources from mixed images. The visual result and correlation coefficient analysis verify that, compared to classical FASTICA, separating precision of WTSCA is higher, and separating effect is better.

Key words: wavelet transformation, sparse component analysis, blind source separation, sparseness, independent component analysis

CLC Number:

TP391

. Blind Image Separation Based on Wavelet Transformation and Sparse Component Analysis[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2010, 33(2): 58-63.

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Blind Image Separation Based on Wavelet Transformation and Sparse Component Analysis

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