A High-Performance Filter for Image Denoising Based on Local Features

Xue Qing Zhao; Xiao Ming Wang; Qiang Liu

doi:10.4028/www.scientific.net/AMM.182-183.496

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 182-183A High-Performance Filter for Image Denoising...

A High-Performance Filter for Image Denoising Based on Local Features

Abstract:

A high-performance filter (HPF) is proposed for removing noises in corrupted images, where the local features are adopted to preserve the image local structures. Firstly, the Chebyshev’s theorem and fuzzy mean process are used to adaptively estimate the detection parameters. Secondly, the local statistics theory is used to estimate noisy pixels, which is based on Radon transform. Thirdly, the PSNR is used as the evaluation metric to show the advantage of the HPF, which is compared with latest filters, such as SBF, HPFSM and the classical filter SMF. Extensive experimental results show that the HPF achieves better performances in removing any kinds of noises (impulse, Gaussian, uniform and mixed), the computational complexity of the HPF is 5 to 11 times lower than the other filters.

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Periodical:

Applied Mechanics and Materials (Volumes 182-183)

Pages:

496-500

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.182-183.496

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Online since:

June 2012

Authors:

Xue Qing Zhao, Xiao Ming Wang, Qiang Liu

Keywords:

Detail Protection, High-Performance Filter, Image Filter, Local Feature, Noise Reduction

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