Wavelet Denoising-Based Image Feature Extraction by Independent Component Analysis without Pre-Whitening

Xing Jia Tang; Xiu Fang Zhang

doi:10.4028/www.scientific.net/AMM.380-384.3830

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 380-384Wavelet Denoising-Based Image Feature Extraction...

Wavelet Denoising-Based Image Feature Extraction by Independent Component Analysis without Pre-Whitening

Abstract:

This paper proposes an ICA image feature extraction method without pre-whitening based on wavelet denoising. Firstly, we conduct denoising for noisy observed images with wavelet transformation; then the feature extraction for denoised observed images is done by using FastICA algorithm without pre-whitening; finally, we further remove the residual noise in extracted feature images with wavelet transformation. Simulation experiment results show that this method is apparent in the performance of denoising, meanwhile, the extracted feature images can distinguish the texture and shape feature well, which has stronger practicability and validity.

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Periodical:

Applied Mechanics and Materials (Volumes 380-384)

Pages:

3830-3833

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.380-384.3830

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Online since:

August 2013

Authors:

Xing Jia Tang, Xiu Fang Zhang

Keywords:

Image Feature Extraction, Independent Component Analysis (ICA), Wavelet Denoising

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