An Image Denoising Algorithm Using Adaptive Multiscale Products Thresholding

Zhang Wen-ge; Liu Fang; Gao Xin-bo; Jiao Li-cheng

doi:10.3724/SP.J.1146.2009.00057

Volume 31 Issue 8

Dec. 2010

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2009 > 31(8): 1779-1785

Zhang Wen-ge, Liu Fang, Gao Xin-bo, Jiao Li-cheng. An Image Denoising Algorithm Using Adaptive Multiscale Products Thresholding[J]. Journal of Electronics & Information Technology, 2009, 31(8): 1779-1785. doi: 10.3724/SP.J.1146.2009.00057

Citation:

Zhang Wen-ge, Liu Fang, Gao Xin-bo, Jiao Li-cheng. An Image Denoising Algorithm Using Adaptive Multiscale Products Thresholding[J]. Journal of Electronics & Information Technology, 2009, 31(8): 1779-1785. doi: 10.3724/SP.J.1146.2009.00057

Citation:

PDF( 1199 KB)

An Image Denoising Algorithm Using Adaptive Multiscale Products Thresholding

doi: 10.3724/SP.J.1146.2009.00057

Received Date: 2009-01-16
Rev Recd Date: 2009-05-21
Publish Date: 2009-08-19

Abstract

Abstract

An adaptive thresholding algorithm for natural image denoising is proposed in this paper, which is based on Stationary Wavelet Transform (SWT) and multiscale products. Different from traditional thresholding denoising algorithm, this threshold is imposed on multiscale products instead of imposed on wavelet coefficients directly. The characteristics of multiscale products of noisy image in SWT domain are analyzed, and an adaptive threshold estimator is proposed. The multiscale products intensify the important structure information of images and weaken the noise, and reach the result of both effective denoising and preserving the edges and details of image simultaneously. Experimental results show that the visual effect and the performance index of proposed algorithm outperform the adaptive multiscale products threshold denoising in dyadic wavelet domain.

FullText(HTML)

References(1)

References

周祚峰, 水鹏朗. 利用数学形态学和方向窗的小波域双重局部维纳滤波图像去噪算法[J].电子与信息学报.2008, 30(4):885-888浏览[2]焦李成, 谭山. 图像的多尺度几何分析: 回顾和进展. 电子学报, 2003, 31(12): 1975-1981.Jiao L C and Tan S. Development and prospect of imagemultiscale geometric analysis. Acta Electronica Sinica, 2003,31(12): 1975-1981.[3]Bao P and Zhang L. Noise reduction for magnetic resonanceimages via adaptive multiscale products thresholding[J].IEEETransactions on Medical Imaging.2003, 22(9):1089-1099[4]Daniel Flores-Tapia, Zahra M K Moussavi, and GabrielThomas. Heart sound cancellation based on multiscaleproducts and linear prediction[J].IEEE Transactions onBiomedical Engineering.2007, 54(2):234-243[5]Daniel Flores-Tapia, Gabriel Thomas, and NiranjanVenugopal, et al.. Semi automatic MRI prostatesegmentation based on wavelet multiscale products. 30thAnnual International IEEE EMBS Conference Vancouver,British Columbia, Canada, August 20-24, 2008: 3020-3023.[6]Besrour R, Lachiri1 Z, and Ellouze1 N. Detection of anelectrocardiogram R wave based on the multiscale products:MIT/BIH arrhythmia and Qt databases evaluation. 2007,ISSPA 2007. 9th International Symposium on SignalProcessing and its Applications, Sharjah, United ArabEmirates (U.A.E.), 12-15 Feb. 2007: 1-4.[7]Solb?S and Eltoft T. A stationary wavelet-domain wienerfilter for correlated speckle[J].IEEE Transactions onGeoscience and Remote Sensing.2008, 46(4):1219-1230[8]Guarnizo C, Orozco A A, and Castellanos G. Microelectrodesignals segmentation using stationary wavelet transform.2008 International Conference on BioMedical Engineeringand Informatics, Sanya, Hainan, China, 27-30 May 2008, Vol.2: 450-454.[9]Galiana-Merino J J, Rosa-Herranz J L, and Parolai S.Seismic P phase picking using a Kurtosis-based criterion inthe stationary wavelet domain[J].IEEE Transactions onGeoscience and Remote Sensing.2008, 46(11):3815-3826

Relative Articles

Supplements(0)

Cited By

Proportional views