An Improvement of Non-Local Means Denoising Method in the Presence of Large Noise

Musab Elkheir Salih; Xu Ming Zhang; Ming Yue Ding

doi:10.4028/www.scientific.net/AMM.263-266.223

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 263-266An Improvement of Non-Local Means Denoising Method...

An Improvement of Non-Local Means Denoising Method in the Presence of Large Noise

Abstract:

The performance of singular value decomposition (SVD) based nonlocal mean (NLM) denoising method degrades when the noise is high. This paper describes an approach of an improvement of NLM denoising when the noise is large. Instead of SVD, we combine the kernel principal component analysis (KPCA) with NLM. It is demonstrated in terms of peak signal to noise ratio (PSNR) in decibels (dB) that the NLM denoising method is improved using various test images corrupted by large additive white Gaussian noise (AWGN)

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Periodical:

Applied Mechanics and Materials (Volumes 263-266)

Pages:

223-226

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.263-266.223

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Online since:

December 2012

Authors:

Musab Elkheir Salih, Xu Ming Zhang, Ming Yue Ding

Keywords:

AWGN, KPCA, NLM, PSNR, SVD

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