Bivariate Shrinkage using Undecimated Dual-tree Complex Wavelet Transform for Image Denoising
Bivariate Shrinkage using Undecimated Dual-tree Complex Wavelet Transform for Image Denoising
复制标题
使用未抽取双树复小波变换进行双变量收缩进行图像去噪
DOI：
10.3233/ifs-152037
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发表时间：
--
影响因子：
2
通讯作者：
Guowei Yang
中科院分区：
文献类型：
--
作者：
Peng Yang;Guowei Yang
本文提出了一种基于双变量收缩的加性白色高斯噪声图像去噪方法。该方法首先对含噪图像进行非抽取双树复小波变换，使所有尺度上的同位复小波系数保持直接的一一对应关系。然后，在非高斯二元模型下，考虑父子相关性估计小波系数，完成去噪过程。对测试图像的实验结果表明，该方法不仅可以消除不同程度的噪声，而且可以获得精细结构的保护。
In this paper, we present a bivariate shrinkage method for denoising of images corrupted with additive white Gaussian noise. The proposed method first employs undecimated dual-tree complex wavelet transform on noisy image, which can keep a direct one-to-one relationship between the co-located complex wavelet coefficients at all scales. After that, it estimates the wavelet coefficients by taking into account the parent-child dependency under the non-Gaussian bivariate model, and completes the denoising procedure. Experimental results on test images show that our method can not only eliminate different levels of noise but also obtain fine structures preservation.