小波阈值图像去噪算法改进

高文仲; 陈志云; 曾秋梅

小波阈值图像去噪算法改进

1.
华东师范大学计算中心,上海 200062

详细信息

中图分类号: TN911.73
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- 文章访问数: 1628
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- 被引次数: 0
出版历程
- 收稿日期: 2012-09-01
- 修回日期: 2013-01-01
- 刊出日期: 2013-11-25

Improved algorithm of wavelet thresholding for image denoising

1.
Computer Center, East China Normal University, Shanghai 200062, China

摘要

摘要: 对小波阈值收缩图像去噪算法进行了研究，在软阈值函数的基础上提出了一种改进的阈值函数，算法中采用BayesShrink阈值和SureShrink阈值，一定程度上抑制了SureShrink阈值的过保留小波系数.与传统方法（软阈值函数法(BayesShrink阈值)、软阈值函数法(SureShrink阈值)、硬阈值函数法以及半软阈值函数去噪法）相比，在处理边缘点不多的图像时，改进的阈值函数方法处理后的图像具有更高的峰值信噪比(PSNR)和信噪比(SNR)，并具有更低的均方误差(MSE)，图像更加清晰.
- 小波变换 /
- 阈值 /
- 图像去噪
Abstract: The algorithm of the image denoising based on wavelet thresholding shrinkage has been studied. On this basis of soft thresholding function, an improved thresholding function was proposed. The improved thresholding function uses BayesShrink thresholding and SureShrink thresholding to suppress the too many reservations of wavelet coefficients which are produced by SureShrink thresholding. Compared with traditional algorithm (the soft threshold function method (BayesShrink thresholding), the soft threshold function method (SureShrink thresholding), the hard threshold function method and the semi-soft threshold function method), in dealing with the image with few edge points, the processed image based on the improved algorithm has a higher Peak Signal to Noise Ratio(PSNR), a higher Signal to Noise Ratio(SNR), and a lower Mean Square Error(MSE). The processed image based on the improved algorithm is clearer.
- wavelet transform /
- threshold /
- image denoising

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参考文献(1)

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