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31.
Image denoising plays an important role in image processing, which aims to separate clean images from the noisy images. A number of methods have been presented to deal with this practical problem in the past decades. In this paper, a sparse coding algorithm using eigenvectors of the graph Laplacian (EGL-SC) is proposed for image denoising by considering the global structures of images. To exploit the geometry attributes of images, the eigenvectors of the graph Laplacian, which are derived from the graph of noised patches, are incorporated in the sparse model as a set of basis functions. Sequently, the corresponding sparse coding problem is presented and efficiently solved with a relaxed iterative method in the framework of the double sparsity model. Meanwhile, as the denoising performance of the EGL-SC significantly depends on the number of the used eigenvectors, an optimal strategy for the number selection is employed. A parameter called as out-of-control rate is set to record the percentage of the denoised patches that suffer from serious residual errors in the sparse coding procedure. Thus, with the eigenvector number increasing, the appropriate number can be heuristically selected when the out-of-control rate falls below an empirical threshold. Experiments illustrate that the EGL-SC can achieve a better performance than some other well-developed denoising methods, especially in the structural similarity index for the noise of large deviations. 相似文献
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《Measurement》2016
Effective application of the Lamb waves for structural health monitoring and damage identification intensively relies on the accurate damage-related feature extraction in the received signals. Most of existing signal processing methods extract the damage-related features from the time–frequency joint spectrum which requires a quite amount of effort. In this paper, the soft-thresholding process, based on different signal decomposition methods, is introduced to damage identification so that the damage-related signal features can be manifested more distinctively. By applying two popular signal decomposition methods (i.e., the discrete wavelet transform (DWT) and the empirical mode decomposition (EMD)), the signal of interest can be represented by a series of components with different frequencies. Since most noises exist in the high frequency range, it is feasible to alleviate noise by restricting the energy of high-frequency components. Finally, a denoised signal is synthesized using the corresponding reconstruction method. As an application, the soft-thresholding process is performed to detect a small crack on an isotropic aluminum plate under the white Gaussian noise contamination. The results, from both the numerical finite element simulation and experimental test, indicate that the soft-thresholding process is capable of effectively reducing the effect of noise, convincingly improving the sensitivity of damage identification, and discriminating relatively small damage. 相似文献
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NonLocal Means (NLM),taking fully advantage of image redundancy,has been proved to be very effective in noise removal.However,high computational load limits its wide application.Based on Principle Comp... 相似文献
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针对水声目标信号检测与识别中,海洋环境噪声的存在所造成的检测性能下降问题,提出一种基于统计小波模型的信号去噪及原信号波形估计方法.通过对舰船声信号以及海洋环境噪声信号特性的分析,根据信号的概率密度函数,推导出信号小波系数的相邻尺度间关系,从而建立信号的去噪模型,对仿真和实测数据的分析都验证了方法的有效性. 相似文献
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针对SAR图像相干斑噪声去除问题,提出了一种基于多尺度分解的Contourlet域K-L变换的SAR图像去噪的新方法。方法首先对源图像进行Contourlet分解,在不同频段的子带图像中,利用K-L变换进行能量保持即提出信号的主要特征,用重构图像来进行去噪,最后通过Contourlet逆变换得到去噪之后的图像。在SAR图像上的实验结果表明,方法不仅较好地保持了图像的纹理和细节特征及边缘特征,且信噪比也较高。 相似文献