Combining Curvelet Transform and Wavelet Transform for Image Denoising

被引：0

作者：

Li, Ying ^{[1
,2
]}

Zhang, Shengwei ^{[2
]}

Hu, Jie ^{[1
]}

机构：

[1] Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China

[2] Natl Key Lab Fire Control Technol, Luoyang 471009, Peoples R China

来源：

ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE | 2010年 / 6216卷

基金：

中国国家自然科学基金;

关键词：

Curvelet transform; Wavelet transform; Image denoising;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Wavelet transform has the good characteristic of time-frequency locality and many researches show that it can perform well for denoising in smooth and singular areas. But it isn't suitable for describing the signals, which have high dimensional singularities. Curvelet is one of new multiscale transform theories, which possess directionality and anisotropy, and it breaks some inherent limitations of wavelet in representing directions of edges in image. So it has superiority in some image analysis, such as image denoising. This paper proposes a new method for denoising, which combines curvelet transform and wavelet transform. The experiment indicates that this method has better performance.

引用

页码：317 / +

页数：3

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