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Sparse_Lowrank Denoise

于 2018-07-01 发布文件大小:37KB

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下载积分: 1 下载次数: 20

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代码说明：

稀疏低秩去噪的MATLAB代码，包括OMP算法与KSVD算法(Sparse low rank denoising MATLAB codes, including OMP algorithm and KSVD algorithm)

文件列表：

IGARSS2013\cal_ssim.m, 6372 , 2010-04-15
IGARSS2013\Compute_NLM_Matrix.m, 2004 , 2013-03-31
IGARSS2013\FeatureSIM.m, 18790 , 2012-10-31
IGARSS2013\KSVD_Matlab_ToolBox2\demo1.m, 1907 , 2009-09-03
IGARSS2013\KSVD_Matlab_ToolBox2\demo2.m, 3679 , 2013-04-23
IGARSS2013\KSVD_Matlab_ToolBox2\demo3.m, 8504 , 2006-12-28
IGARSS2013\KSVD_Matlab_ToolBox2\denoiseImageDCT.m, 5426 , 2007-01-24
IGARSS2013\KSVD_Matlab_ToolBox2\denoiseImageGlobal.m, 6046 , 2006-12-12
IGARSS2013\KSVD_Matlab_ToolBox2\denoiseImageKSVD.m, 9090 , 2013-03-31
IGARSS2013\KSVD_Matlab_ToolBox2\displayDictionaryElementsAsImage.m, 3224 , 2007-01-25
IGARSS2013\KSVD_Matlab_ToolBox2\gererateSyntheticDictionaryAndData.m, 1896 , 2006-12-11
IGARSS2013\KSVD_Matlab_ToolBox2\KSVD.m, 12292 , 2009-09-03
IGARSS2013\KSVD_Matlab_ToolBox2\KSVD_NN.m, 11585 , 2006-12-28
IGARSS2013\KSVD_Matlab_ToolBox2\MOD.m, 8053 , 2006-12-12
IGARSS2013\KSVD_Matlab_ToolBox2\my_im2col.m, 631 , 2006-12-11
IGARSS2013\KSVD_Matlab_ToolBox2\NN_BP.m, 1105 , 2006-12-24
IGARSS2013\KSVD_Matlab_ToolBox2\OMP.m, 954 , 2007-04-29
IGARSS2013\KSVD_Matlab_ToolBox2\OMPerr.m, 1087 , 2013-04-22
IGARSS2013\KSVD_Matlab_ToolBox2\README.txt, 4802 , 2006-12-28
IGARSS2013\maindenoise.m, 1546 , 2014-08-28
IGARSS2013\parallsparsecoding.m, 488 , 2013-05-20
IGARSS2013\soft.m, 119 , 2009-09-24
IGARSS2013\spa_code.m, 2273 , 2014-08-28
IGARSS2013\Untitled.m, 2364 , 2014-08-28
IGARSS2013\KSVD_Matlab_ToolBox2, 0 , 2014-08-28
IGARSS2013, 0 , 2014-08-28

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asifnaxc
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scikit-image: Image processing in Python：Python编写的图像处理程序，可直接调用，是学习python图像处理的很好参考。(scikit-image: Image processing in Python：Image processing program written in Python, can be called directly, is to learn Python a good reference to the image processing.)

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2021-03-30 17:42:23下载

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2009-05-27 17:00:50下载

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首先将非平稳的故障振动信号进行双树复小波包分解，得到不同频带的分量；然后对每个分量求其峭度值和相关系数并进行比较；最后选取峭度值和相关系数较大的分量进行软阈值降噪和双树复小波包重构，即可有效地消除振动信号中噪声的干扰，同时保留信号中的有效信息即实现了故障特征信息的提取。(In view of the above situation, a new fault diagnosis method is proposed based on dual-tree complex wavelet packet transform and threshold de-noising. Firstly, the non-stationary fault signal is decomposed into several different frequency band components through dual-tree complex wavelet packet decomposition. Secondly, Kurtosis and the cross-correlation coefficient of each component are obtained and compared. Due to the kurtosis reflecting the signal variations, if the kurtosis value is bigger, the degree of the change of signal is bigger too. The correlation coefficient can reflect the proximity between the component and the original signal at the same time, the correlation coefficient is bigger, the more similar with the original signal. Finally, the components that have a bigger value are chosen to be de-noised by a soft threshold and reconstructed by dual-tree complex wavelet packet transform. The noise interference was eliminated effectively, and the effective si)

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