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基于中值變換和金字塔分解的圖像去噪方法

黃文濤,  畢篤彥,  毛柏鑫,  馬時平

黃文濤, 畢篤彥, 毛柏鑫, 馬時平. 基于中值變換和金字塔分解的圖像去噪方法[J]. 電子與信息學(xué)報, 2004, 26(11): 1686-1692.
引用本文: 黃文濤, 畢篤彥, 毛柏鑫, 馬時平. 基于中值變換和金字塔分解的圖像去噪方法[J]. 電子與信息學(xué)報, 2004, 26(11): 1686-1692.
Huang Wen-tao, Bi Du-yan, Mao Bai-xin, Ma Shi-ping. A Image Denoising Method Based on Median Transform and Pyramid Decomposition[J]. Journal of Electronics & Information Technology, 2004, 26(11): 1686-1692.
Citation: Huang Wen-tao, Bi Du-yan, Mao Bai-xin, Ma Shi-ping. A Image Denoising Method Based on Median Transform and Pyramid Decomposition[J]. Journal of Electronics & Information Technology, 2004, 26(11): 1686-1692.

基于中值變換和金字塔分解的圖像去噪方法

A Image Denoising Method Based on Median Transform and Pyramid Decomposition

  • 摘要: 該文基于中值變換提出了一種信號的非線性多尺度金字塔分解算法，然后給出了一種利用它來對同時含有脈沖噪聲和高斯噪聲圖像的去噪方法。由于圖像經(jīng)過中值金字塔變換后的系數(shù)表現(xiàn)出不同的特點，能對噪聲產(chǎn)生有效的分離，可以對不同的噪聲采用不同的系數(shù)抑制方法來達到去噪的目的。仿真試驗結(jié)果表明本方法很有效，且優(yōu)于其它方法。
    Abstract: A nonlinear multiscalc pyramidal decomposition based on median transform is presented in this paper at first. Then it gives a denoising algorithm which can restore the image distorted by impidse noise and Gaussian noise. The coefficients of the image via the median pyramidal transform represent different characteristics, so the transform can effectively detach noise from image. The different noise coefficients suppression means can be adopted to remove different noise. The simulation result indicates the method is effective, and superior over other methods.
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  • Burt P, Adelson E. The Laplacian pyramid as a compact image code[J].IEEE Trans. on Commun.1983, 31(4):532-540[2]Donoho D L, Yu T P Y. Robust nonlinear wavelet transform based on median-interpolation.Conf. Rec. Thirty-First Asilomar Conf. Signals, Syst. Comput., 1997, 1: 75-79.[3]Starck J L, Murtagh F, Louys M. Astronomical image compression using the pyramidal median transform. IV. ASP Conference Series, 1995, 77(1): 162-165. [4] Melnik V P, Shmulevich I, Egiazarian K, Astola J. Block-median pyramidal transform: analysis and denoising applications. IEEE Trans. on IP, 2001, 49(2): 364-372.[4]Melnik V, Shmulevich I, Egiazarian K, Astola J. Image denoising using a block-median pyramid,in Proc. IEEE Int. Conf. Image Process., Kobe, Japan, 1999: 84-87.[5]Yin L, Yang B, Gabbouj M. Weighted median filters: a tutorial[J].IEEE Trans. Circuits Syst. .1996, 43(3):157-192[6]Donoho D L. Denoising by soft thresholding. IEEE Trans. Info. Theory, 1994, 41(3): 613-627.[7]Zervakis M E, Sundararajan V, Parhi K K. A wavelet-domain algorithm for denoising in the presence of noise outliers. Washington, DC, USA, 1997, 1: 632-635.[8]Chang S G, Yu B, Vetterli M. Spatially adaptive wavelet thresholding with context modeling for image denoising, IEEE Trans. on IP, 2000, 9(9): 1522-1531.[9]Gong W, Shi Q Y, Cheng M D. CB morphology and its applications, Proc. Int. Conf. for Young Computer Scientists, Beijing, 1991: 260-264.
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出版歷程
  • 收稿日期:  2003-06-25
  • 修回日期:  2003-09-25
  • 刊出日期:  2004-11-19

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