Image denoising using SVM classification in nonsubsampled contourlet transform domain
Image denoising using SVM classification in nonsubsampled contourlet transform domain
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DOI：
10.1016/j.ins.2013.05.028
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发表时间：
2013-10
期刊：
影响因子：
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通讯作者：
Xiangyang Wang;Hongying Yang;Yu Zhang;Zhong-Kai Fu
中科院分区：
文献类型：
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作者：
Xiangyang Wang;Hongying Yang;Yu Zhang;Zhong-Kai Fu
对于图像去噪，主要的挑战是如何在提高信噪比的同时保留边缘和纹理等信息承载结构，以获得满意的视觉质量。边缘保持图像去噪已成为一个非常深入的研究课题。本文提出了一种在非下采样轮廓波变换（NSCT）域中使用支持向量机（SVM）分类的图像去噪方法。首先，噪声图像分解成不同的子带的频率和方向响应使用NSCT。然后利用NSCT域的空间规律性构造含噪图像像素的特征向量，并通过训练得到最小二乘支持向量机（LS-M）模型。然后利用LS-SVM训练模型将NSCT细节系数分为边缘相关系数和噪声相关系数两类。最后，利用自适应贝叶斯阈值，对NSCT系数的细节子带进行收缩去噪。大量的实验结果表明，我们的方法可以获得更好的性能，在主观和客观评价方面比那些国家的最先进的去噪技术。特别是，该方法可以很好地保持边缘，同时去除噪声。
For image denoising, the main challenge is how to preserve the information-bearing structures such as edges and textures to get satisfactory visual quality when improving the signal-to-noise-ratio (SNR). Edge-preserving image denoising has become a very intensive research topic. In this paper, we propose an image denoising using support vector machine (SVM) classification in nonsubsampled contourlet transform (NSCT) domain. Firstly, the noisy image is decomposed into different subbands of frequency and orientation responses using the NSCT. Secondly, the feature vector for a pixel in a noisy image is formed by the spatial regularity in NSCT domain, and the least squares support vector machine (LS-M) model is obtained by training. Then the NSCT detail coefficients are divided into two classes (edge-related coefficients and noise-related ones) by LS-SVM training model. Finally, the detail subbands of NSCT coefficients are denoised by using shrink method, in which the adaptive Bayesian threshold is utilized. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than those state-of-the-art denoising techniques. Especially, the proposed method can preserve edges very well while removing noise.