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计算机工程 ›› 2009, Vol. 35 ›› Issue (13): 214-215,. doi: 10.3969/j.issn.1000-3428.2009.13.074

• 图形图像处理 • 上一篇    下一篇

基于小波域最大子节点相关性的图像去噪

刘红毅1,2,韦志辉2   

  1. (1. 南京理工大学理学院,南京 210094;2. 南京理工大学计算机科学与技术学院,南京 210094)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-07-05 发布日期:2009-07-05

Image Denoising Based on Maxima Child Node Dependence Coefficients in Wavelet Domain

LIU Hong-yi1,2, WEI Zhi-hui2   

  1. (1. School of Science, Nanjing Uiversity of Science and Technology, Nanjing 210094; 2. School of Computer Science and Technology, Nanjing University of Sience and Technology, Nanjing 210094)
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-07-05 Published:2009-07-05

摘要: 图像经过小波分解后,真实信号的小波系数之间有很强的相关性,而噪声的小波系数之间的相关性较弱。此外大幅值的小波系数反映了图像的边缘信息。利用小波系数尺度间的关系以及大幅值小波系数,提出基于最大子节点2种相关系数的图像去噪方法。实验结果表明,该方法在去噪和保持纹理及边缘方面都明显优于传统相关系数去噪方法。

关键词: 小波变换, 相关系数, 去噪

Abstract: Wavelet coefficients of real signal have strong relations, while the dependence of coefficients of noise is more weaker. On the other hand, large wavelet coefficients contain the information of image edge. Considering the relationship of intrascale and maxima coefficients, two new dependence coefficients are proposed. Experimental results show that the new denoising method performs better both in denoising and preserving texture, compared to the traditional denosing metohd based ondependence coefficients.

Key words: wavelet transform, dependence coefficients, denoising

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