| 曲波域经验Wiener滤波 |
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| 引用本文： | 李伟,杨航.曲波域经验Wiener滤波[J].吉林大学学报(理学版),2013,51(2):312-316. |
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| 作者姓名： | 李伟 杨航 |
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| 作者单位： | 1. 吉林大学 数学学院, 长春 130012,2. 中国科学院 长春光学精密机械与物理研究所, 长春 130033 |
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| 基金项目： | 吉林省科技厅重点项目基金(批准号:20090314) |
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| 摘 要： | 利用全变差(TV)估计, 设计一种曲波域Wiener滤波, 提出一种新的基于曲波的图像去噪算法. 该算法结合了曲波的优点和TV模型对图像边界的保持能力：TV模型用来设计经验Wiener滤波, 在曲波域实现应用. 数值实验表明, 该方法比曲波收缩和基于TV模型的方法效果更好.
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| 关 键 词： | 图像去噪 曲波 Wiener滤波 |
| 收稿时间： | 2012-08-22 |
Curvelet Domain Empirical Wiener Filter |
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| LI Wei,YANG Hang.Curvelet Domain Empirical Wiener Filter[J].Journal of Jilin University: Sci Ed,2013,51(2):312-316. |
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| Authors: | LI Wei YANG Hang |
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| Institution: | 1. College of Mathematics, Jilin University, Changchun 130012, China|2. Changchun Instituteof Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China |
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| Abstract: | The purpose of this letter is to develop a new curveletdenoising algorithm for denoising images corrupted with additive white Gaussiannoise (AWGN). We used a total variation(TV) estimate as means todesign a curvelet domain Wiener filter. The TV estimate indirectly yields the estimate of the image that is leveraged into the design of the filter. A peculiaraspect of this method is its use of TV and curvelet base, i.e., the TV for the designof the empirical Wiener filter and curvlet base for its application. Numericalexamples demonstrate that our method can perform better than curvelet shrinkageand TV based method. Curvelets have been mathematically proven to represent distributed discontinuities such as edges better than traditional wavelets. |
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| Keywords: | image denoising curvelet Wiener filter |
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