Image denoising via bivariate shrinkage function based on a new structure of dual contourlet transform
Image denoising via bivariate shrinkage function based on a new structure of dual contourlet transform
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基于双轮廓波变换新结构的双变量收缩函数图像去噪
DOI：
10.1016/j.sigpro.2014.10.017
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发表时间：
2015
影响因子：
4.4
通讯作者：
Yide Ma
中科院分区：
文献类型：
--
作者：
Min Dong;Jiuwen Zhang;Yide Ma
图像去噪是图像处理的一个基本步骤，图像去噪的目的是完全去除噪声，同时很好地保留图像的边界和纹理信息。然而，传统的滤波方法容易导致纹理和细节信息的丢失。针对这一问题，本文提出了一种新的图像去噪方法，首先在轮廓波变换和对偶树复小波变换的基础上，提出了一种新的结构对偶轮廓波变换（DCT）。DCT采用对偶树拉普拉斯金字塔（LP）变换来提高平移不变性，并采用方向滤波器组（DFB）来实现更高的方向选择性。与现有的其他多分辨率分析结构相比，DCT的主要优点是它不仅具有其他结构的优点，而且结构简单，易于实现。最值得注意的是，DCT的冗余度最多为8/3；这是其他现存结构所羡慕的。其次，在研究了DCT系数的分布以及尺度间和尺度内依赖关系之后，我们考虑了DCT系数的去噪，并对DCT系数使用二元阈值函数。仿真实验表明，该方法在峰值信噪比（PSNR）和视觉质量方面都优于现有的去噪算法。此外，为了验证我们方法的有效性，我们给出了原始图像与其他去噪文献中很少使用的去噪图像之间的差异。
Image denoising is a basic procedure of image processing, and the purpose of image denoising is to remove noises entirely and well preserve image boundaries and texture information simultaneously. However, conventional filtering methods easily lead to the loss of texture and details information. This paper proposes a new image denoising method to improve this problem, first proposing a new structure called dual contourlet transform (DCT) which is improved from contourlet transform and dual tree complex wavelet transform (DTCWT). The DCT employs a dual tree Laplacian Pyramid (LP) transform to improve the shift invariance and adopts directional filter banks (DFB) to achieve higher directional selectivity. Compared to other existing structures of multiresolution analysis, the main advantage of the DCT is that it not only possesses the advantages of other structures, but also it has simple structure and easy to implement. The most noteworthy is the redundancy of DCT is 8/3 at most; it is the envy of other existing structures. Second, after studying the distribution of DCT coefficients and the correlation between the interscale and intrascale dependencies, we take this account into denoising and use bivariate threshold function on DCT coefficients. Simulation experiments show that the proposed method achieves better performance than those outstanding denoising algorithms in terms of peak signal-to-noise ratio (PSNR), as well as visual quality. In addition, to verify the validity of our method, we give the difference between the original image and the denoised image that rarely used in other denoising literatures.
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影响因子：
8
作者：
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DOI：
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