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应用科学学报 ›› 2012, Vol. 30 ›› Issue (3): 275-280.doi: 10.3969/j.issn.0255-8297.2012.03.010

• 论文 • 上一篇    下一篇

结合Tetrolet 与主动随机场模型的高斯噪声抑制

王锐玲， 施俊   

  1. 上海大学通信与信息工程学院，上海200072
  • 收稿日期:2011-03-17 修回日期:2011-04-30 出版日期:2012-05-30 发布日期:2012-05-30
  • 通信作者: 施俊，副教授，研究方向：医学图像处理，E-mail: junshi@staff.shu.edu.cn
  • 作者简介:施俊，副教授，研究方向：医学图像处理，E-mail: junshi@staff.shu.edu.cn
  • 基金资助:

    国家自然科学基金(No.60701021)；上海市教育委员会科研创新项目基金(No.09YZ15)；上海市教委重点学科建设项目基金(No.J50104)资助

Gaussian Noise Reduction with Tetrolet and Active Random Field Model

WANG Rui-ling, SHI Jun   

  1. School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China
  • Received:2011-03-17 Revised:2011-04-30 Online:2012-05-30 Published:2012-05-30

PDF

1378

被引次数

3

摘要：

文中提出一种结合Tetrolet 变换和主动随机场模型的去噪方法，用于抑制图像中的高斯噪声. 对含有高斯噪声的图像进行Haar 小波分解，在小波变换域利用主动随机场算法针对高斯噪声进行去噪，并利用小波逆变换重构去噪后的图像，最后用Tetrolet 变换在变换域进一步抑制噪声. 实验结果表明，与直接利用小波、Tetrolet、马尔科夫随机场模型以及主动随机场模型等方法相比，该方法对添加不同程度高斯噪声的图像有更好的去噪效果.

关键词: 图像去噪, 高斯噪声, Tetrolet 变换, 主动随机场模型

Abstract:

In this study, an image denoising algorithm is proposed by combining Tetrolet transform and active random field (ARF). An image with Gaussian noise is decomposed with Haar wavelets, and the ARF algorithm is used to reduce Gaussian noise in the wavelet domain. After inverse wavelet transform, the Tetrolet transform is used for further denoising. The proposed method is compared with other denoising algorithms including wavelet algorithm, Tetrolet algorithm, Markov random field based algorithm and ARF based algorithm. Experimental results indicate that the proposed approach can effectively reduce Gaussian noise at various levels, and achieve better results than other algorithms.

Key words: Tetrolet transform, active random field model, image denoising, Gaussian noise

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