|
|||||
|
|
| 基于有理数阶微分的图像去噪新方法 | |
| 引用本文： | 蒋 伟,李小龙,杨永琴,张 恒.基于有理数阶微分的图像去噪新方法[J].计算机应用,2014,34(3):801-805. |
| 作者姓名： | 蒋 伟 李小龙 杨永琴 张 恒 |
| 作者单位： | 1. 重庆交通大学 理学院，重庆400074 2. 重庆邮电大学 自动化学院，重庆400065； 3. 重庆交通大学 土木建筑学院，重庆400074 |
| 基金项目： | 国家863计划项目;重庆市自然科学基金资助项目;重庆市教委科研基金资助项目 |
| 摘 要： | 针对现有的全变分(TV)去噪方法效果不太理想，在去噪的同时不能较好地保持图像的边缘和纹理细节，提出了一种基于有理数阶微分的图像去噪新方法。首先详细地讨论了现有的全变分去噪方法和分数阶微分去噪方法各自的优缺点；然后将全变分去噪模型与分数阶微分理论相结合，获得有理数阶微分图像去噪新模型，并推导了相应的有理数阶微分模板。实验结果表明：与改进前的方法相比，信噪比(SNR)提高了接近2个百分点，较好地传承了全变分去噪方法对图像高频部分大幅改善及分数阶微分去噪方法能够很好地保留图像纹理细节的优点，是一种有效的图像去噪方法。 |
| 关 键 词： | 图像去噪 全变分 有理数阶微分 纹理细节 客观评价指标 |
| 收稿时间： | 2013-09-12 |
| 修稿时间： | 2013-11-19 |
New image denoising method based on rational-order differential |
|
| JIANG Wei LI Xiaolong YANG Yongqing ZHANG Heng.New image denoising method based on rational-order differential[J].journal of Computer Applications,2014,34(3):801-805. | |
| Authors: | JIANG Wei LI Xiaolong YANG Yongqing ZHANG Heng |
| Affiliation: | 1. School of Science, Chongqing Jiaotong University, Chongqing 400074, China; 2. College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; 3. College of Civil Engineering and Construction, Chongqing Jiaotong University, Chongqing 400074, China |
| Abstract: | The effect of the existing Total Variation (TV) method for image denoising is not ideal, and it is not good at keeping the characteristics of image edge and texture details. A new method of image denoising based on rational-order differential was proposed in this paper. First, the advantages and disadvantages of the present image denoising methods of TV and fractional differential were discussed in detail, respectively. Then, combining the model of TV with fractional differential theory, the new method of image denoising was obtained, and a rational differential mask in eight directions was drawn. The experimental results demonstrate that compared with the existing denoising methods, Signal Noise Ratio (SNR) is increased about 2 percents, and the method retains effectively the advantages of integer and fractional differential methods, respectively. In aspects of improving significantly high frequency of image and keeping effectively the details of image texture, it is also an effective, superior image denoising method. Therefore, it is an effective method for edge detection. |
| Keywords: | Image denoising Total variation Rational-order differential Texture detail Objective evaluation index |
| 本文献已被 CNKI 等数据库收录！ | |
| 点击此处可从《计算机应用》浏览原始摘要信息 | |
| 点击此处可从《计算机应用》下载全文 | |