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计算机工程 ›› 2009, Vol. 35 ›› Issue (5): 209-211,. doi: 10.3969/j.issn.1000-3428.2009.05.072

• 图形图像处理 • 上一篇    下一篇

高阶各向异性扩散小波收缩图像降噪算法

朱景福1,2,黄凤岗1   

  1. (1. 哈尔滨工程大学计算机科学与技术学院,哈尔滨 150001;2. 黑龙江八一农垦大学信息技术学院,大庆 163319)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-03-05 发布日期:2009-03-05

High-order Anisotropic Diffusion Wavelet Shrinkage Image Denoising Algorithm

ZHU Jing-fu1,2, HUANG Feng-gang1   

  1. (1. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001; 2. College of Information Technology, Heilongjiang August First Land Reclamation University, Daqing 163319)
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-03-05 Published:2009-03-05

摘要: 证明一种高阶各向异性扩散与小波收缩的等价性,并根据等价性利用高阶各向异性扩散与小波收缩的优势,提出高阶各向异性扩散小波收缩降噪算法。该算法在低频部分采用经典的非线性扩散方法进行扩散,在高频部分采用高阶各向异性扩散方法进行小波收缩。实验结果表明,高阶各向异性扩散小波收缩算法的计算复杂性介于高阶各向异性扩散与小波收缩算法之间,降噪能力高于这2种方法。

关键词: 图像降噪, 高阶各向异性扩散, 小波收缩

Abstract: This paper proves the equivalency between a high-order anisotropic diffusion and wavelet shrinkage. Based on the equivalency, it puts forward the high-order anisotropic diffusion wavelet shrinkage image denoising algorithm utilizing the advantage of high-order anisotropic diffusion and wavelet shrinkage respectively. In the algorithm, it adopts the classic nonlinear diffusion method(PM) to process the low frequency part, diffuse the high frequency part using high order anisotropic diffusion to shrink the wavelet coefficient. Experimental results show that the computational complexity of high-order anisotropic diffusion wavelet shrinkage algorithm is less than the high-order anisotropic diffusion and more than wavelet shrinkage, the denoising ability is better than the two algorithms.

Key words: image denoising, high-order anisotropic diffusion, wavelet shrinkage

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