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传统小波去噪虽然可在一定程度上去除噪声对原始信号的干扰,但去噪效果并不理想。针对传统小波去噪中存在的问题,提出一种改进的小波去噪方法,并将改进小波去噪与EEMD-HHT有机结合,进而提出一种基于改进小波去噪的EEMD—HHT信号处理新方法。基于MATLAB软件,分别利用EEMD-HHT方法、基于传统小波去噪的EEMD-HHT信号处理方法和基于改进小波去噪的EEMD-HHT信号处理方法对外圈故障滚动轴承进行故障诊断试验,试验结果与理论计算结果对比分析表明,基于改进小波去噪的EEMD-HHT信号处理方法最为有效。 相似文献
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引入了基于提升法的自适应离散小波交换,使伯恩斯坦预测算子自适应匹配特定的数据序列,并将其应用于改进的信号去噪方法中.仿真实验表明,基于自适应提升小波变换的改进方法同一般的小波变换相比,去噪后的信号信噪比更高,且提升方法的设计灵活、计算简单. 相似文献
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非线性阈值自调整小波图像去噪方法研究 总被引:2,自引:12,他引:2
为解决小波变换阙值去噪方法中阙值的合理选取,提出一种基于非线性阙值自调整小波变换的图像去噪方法。在传统小波阈值去噪方法的基础上,结合神经网络的非线性双曲线正切函数和BP训练方法,首先对含噪图像进行二进小波分解,然后对分解系数进行小波重建,并将重建系数在BP神经网络中采用最速梯度下降法进行优化处理,得到最优阈值,最后对阈值处理的重建系数进行叠加,得到原始图像信号的估计值,即去噪后的图像信号。仿真实验表明,该方法具有较好的重建图像视觉效果,信噪比(SNR)和峰值信噪比(PSNR)均比传统小波阈值方法提高了1~2dB。 相似文献
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基于平移不变的小波变换去噪快速算法 总被引:5,自引:3,他引:2
在介绍小波变换去噪理论的基础上,针对传统的小波变换去噪算法的缺陷,提出了一种基于平移不变的小波去噪快速算法.着重探讨平移不变小波去噪方法中平移量的选取对去噪效果以及计算复杂度的影响.仿真实验证明该算法能够在不影响去噪效果的前提下,较大程度降低计算复杂度. 相似文献
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一种基于小波-Contourlet变换的图像去噪算法 总被引:1,自引:2,他引:1
提出了一种基于小波-Contourlet变换的图像去噪算法.实验证明,该算法相对于小波变换和Contourlet变换能更稀疏的表达图像,并利用此优越性进行图像去噪,可以达到更好的效果和更高的PSNR值. 相似文献
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This paper presents a novel image denoising algorithm based on the modeling of wavelet coefficients with an anisotropic bivariate Laplacian distribution function. The anisotropic bivariate Laplacian model not only captures the child-parent dependency between wavelet coefficients, but also fits the anisotropic property of the variances of wavelet coefficients in different scales of natural images. With this statistical model, we derive a closed-form anisotropic bivariate shrinkage function in the framework of Bayesian denoising and a new image denoising approach with local marginal variance estimation based on this newly derived shrinkage function is proposed in the discrete wavelet transform (DWT) domain. The proposed anisotropic bivariate shrinkage approach is also extended to the dual-tree complex wavelet transform (DT-CWT) domain to further improve the performance of image denoising. To take full advantage of DT-CWT, a more accurate noise variance estimator is proposed and the way the anisotropic bivariate shrinkage function applied to the magnitudes of DT-CWT coefficients is presented. Experiments were carried out in both the DWT and the DT-CWT domain to validate the effectiveness of the proposed method. Using a representative set of standard test images corrupted by additive white Gaussian noise, the simulation results show that the proposed method provides promising results and is competitive with the best wavelet-based denoising results reported in the literature both in terms of peak signal-to-noise ratio (PSNR) and in visual quality. 相似文献
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Noise reduction for magnetic resonance images via adaptive multiscale products thresholding 总被引:16,自引:0,他引:16
Edge-preserving denoising is of great interest in medical image processing. This paper presents a wavelet-based multiscale products thresholding scheme for noise suppression of magnetic resonance images. A Canny edge detector-like dyadic wavelet transform is employed. This results in the significant features in images evolving with high magnitude across wavelet scales, while noise decays rapidly. To exploit the wavelet interscale dependencies we multiply the adjacent wavelet subbands to enhance edge structures while weakening noise. In the multiscale products, edges can be effectively distinguished from noise. Thereafter, an adaptive threshold is calculated and imposed on the products, instead of on the wavelet coefficients, to identify important features. Experiments show that the proposed scheme better suppresses noise and preserves edges than other wavelet-thresholding denoising methods. 相似文献
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维纳滤波和非线性扩散相结合的图像去噪 总被引:1,自引:0,他引:1
提出一种基于小波和非线性扩散的新的图像去噪算法。小波域局部维纳滤波是一种简单有效的去噪方法,利用该方法先对原始图像进行初步去噪,以此引导非线性扩散模型中的边缘检测函数,再用非线性扩散进行去噪。实验表明:该算法不仅很好地保存了图像的边缘信息,而且有效地去除了图像中的大部分噪声,无论是视觉效果还是客观标准上都优于单纯的小波域维纳滤波或非线性扩散去噪。 相似文献
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提出了一种基于正交小波变换的图像去噪方法,首先利用离散小波对图像信号按Mallat算法进行分解,然后采用软闽值与小波重构的算法进行去噪。深入研究了小波变换中的图像分解与重构的Mallat算法,详细介绍了正交小波变换中阈值的选取,并进行了实验研究。实验结果表明,该方法可以有效去除噪声,并保留了图像细节部分的有用信息。 相似文献
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Edge-preserving image regularization based on morphological wavelets and dyadic trees 总被引:1,自引:0,他引:1
Despite the tremendous success of wavelet-based image regularization, we still lack a comprehensive understanding of the exact factor that controls edge preservation and a principled method to determine the wavelet decomposition structure for dimensions greater than 1. We address these issues from a machine learning perspective by using tree classifiers to underpin a new image regularizer that measures the complexity of an image based on the complexity of the dyadic-tree representations of its sublevel sets. By penalizing unbalanced dyadic trees less, the regularizer preserves sharp edges. The main contribution of this paper is the connection of concepts from structured dyadic-tree complexity measures, wavelet shrinkage, morphological wavelets, and smoothness regularization in Besov space into a single coherent image regularization framework. Using the new regularizer, we also provide a theoretical basis for the data-driven selection of an optimal dyadic wavelet decomposition structure. As a specific application example, we give a practical regularized image denoising algorithm that uses this regularizer and the optimal dyadic wavelet decomposition structure. 相似文献
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曾敬枫 《智能计算机与应用》2016,(4):75-77
通过介绍小波图像去噪的方法和小波阈值去噪的步骤,讨论小波基在小波阈值去噪中的作用,阐述了常见的几种小波基的特征及其相关性质的比较。最后通过在MATLAB下,分别选择了db2和sym4两种小波基,进行小波阈值去噪实现图像高频系数的滤波并重建,得到采用不同的小波基影响图像去噪效果的结论。 相似文献
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Image Denoising Using Trivariate Shrinkage Filter in the Wavelet Domain and Joint Bilateral Filter in the Spatial Domain 总被引:3,自引:0,他引:3
Hancheng Yu Li Zhao Haixian Wang 《IEEE transactions on image processing》2009,18(10):2364-2369
This correspondence proposes an efficient algorithm for removing Gaussian noise from corrupted image by incorporating a wavelet-based trivariate shrinkage filter with a spatial-based joint bilateral filter. In the wavelet domain, the wavelet coefficients are modeled as trivariate Gaussian distribution, taking into account the statistical dependencies among intrascale wavelet coefficients, and then a trivariate shrinkage filter is derived by using the maximum a posteriori (MAP) estimator. Although wavelet-based methods are efficient in image denoising, they are prone to producing salient artifacts such as low-frequency noise and edge ringing which relate to the structure of the underlying wavelet. On the other hand, most spatial-based algorithms output much higher quality denoising image with less artifacts. However, they are usually too computationally demanding. In order to reduce the computational cost, we develop an efficient joint bilateral filter by using the wavelet denoising result rather than directly processing the noisy image in the spatial domain. This filter could suppress the noise while preserve image details with small computational cost. Extension to color image denoising is also presented. We compare our denoising algorithm with other denoising techniques in terms of PSNR and visual quality. The experimental results indicate that our algorithm is competitive with other denoising techniques. 相似文献
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论述了小波分解与重构法和非线性小波变换阈值法两种小波去噪方法。论述了一种应用于短期负荷预测中的伪数据处理方法:首先,利用小波变换将负荷序列投影到不同的尺度上;然后,在不同的尺度域分别计算模极大值,并根据负荷以天为周期波动的特性对模极大值进行处理;最后,通过小波重构得到去除伪数据的负荷序列。对实际负荷数据的计算表明了该方法的有效性。 相似文献