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Proceedings Paper

Removal of spatially correlated noise by independent component analysis
Author(s): XiangYan Zeng; Yen-Wei Chen; Zensho Nakao; Deborah van Alphen
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Paper Abstract

In this paper, we apply independent component analysis (ICA) to the reduction of spatially correlated additive noise in images. We take a degraded image as the mixture of the noise and the original image, which are statistically independent. From a view of blind signal separation, we try to restore the original image from two linear mixtures. Motivated by the fact that autocorrelation exists in the neighborhoods of the image and the noise; we design another mixture using the diffusion equation. Then we employ independent component analysis to separate the image and the noise from the two mixtures. Simulation experiments are carried out to remove the Poisson noise from images. Experimental results indicate and impressive performance of the proposed method. Furthermore, the proposed method can be combined with the Wavelet Shrinkage method to improve the denoising performance.

Paper Details

Date Published: 23 February 2005
PDF: 7 pages
Proc. SPIE 5673, Applications of Neural Networks and Machine Learning in Image Processing IX, (23 February 2005); doi: 10.1117/12.593799
Show Author Affiliations
XiangYan Zeng, California State Univ./Northridge (United States)
Yen-Wei Chen, Ritsumeikan Univ. (Japan)
Zensho Nakao, University of the Ryukyus (Japan)
Deborah van Alphen, California State Univ./Northridge (United States)


Published in SPIE Proceedings Vol. 5673:
Applications of Neural Networks and Machine Learning in Image Processing IX
Nasser M. Nasrabadi; Syed A. Rizvi, Editor(s)

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