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KSVD-master

于 2020-12-22 发布
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下载积分: 1 下载次数: 3
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代码说明：

说明：  稀疏表示常用matlab代码，可以直接跑(Sparse representation commonly used matlab code, can run directly)

文件列表：

KSVD-master, 0 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox, 0 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\KSVD.m, 12292 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\KSVD.m~, 12293 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\KSVD_NN.m, 11585 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\MOD.m, 8053 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\NN_BP.m, 1105 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\OMP.m, 954 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\OMPerr.m, 1084 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\OMPerr_checkerboard.m, 1217 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\README.txt, 4802 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\barbara.png, 185727 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\boat.png, 177762 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\demo1.m, 1907 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\demo2.m, 4644 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\demo2.m~, 4544 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\demo3.m, 8504 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\denoiseImageDCT.m, 5679 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\denoiseImageGlobal.m, 6290 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\denoiseImageKSVD.m, 9473 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\denoiseImageKSVD.m~, 9503 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\dic_dct.png, 33483 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\dic_global.png, 34245 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\dic_self.png, 48744 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\displayDictionaryElementsAsImage.asv, 3246 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\displayDictionaryElementsAsImage.m, 3224 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\gererateSyntheticDictionaryAndData.m, 1896 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\globalTrainedDictionary.mat, 5749450 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\house.png, 34985 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\lena.png, 151199 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\my_im2col.m, 631 , 2015-12-15
KSVD-master\KSVD_Matlab_ToolBox\peppers256.png, 40181 , 2015-12-15
KSVD-master\barbara.png, 185727 , 2015-12-15
KSVD-master\denoiseGaussianByRepeating.m, 783 , 2015-12-15
KSVD-master\denoiseSaltPepperTest.m, 781 , 2015-12-15
KSVD-master\testDemosaicing.m, 80 , 2015-12-15
KSVD-master\test_MedianFilterForSaltPepper.m, 201 , 2015-12-15
KSVD-master\test_samplingReduction.m, 274 , 2015-12-15

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    积分：1
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    压缩包里面为2个程序，一个是悬链线模拟索的程序，一个是抛物线法模拟的索的程序(Compressed inside of two programs, one cable catenary simulation program, a parabolic law analog cable programs)
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    请将本压缩文件包下的所有文件放在AutoCAD的支 持目录之下，如 AutoCAD 的安装目录下；或者在下拉 菜单中的 工具-> 选项 －> 文件 -> 支持文件搜索路 径 -> 添加 本程序所在目录。调入应用程序jgj.vlx ， 可以直接将本程序拖入AutoCAD绘图区域，或者在下拉 菜单的 工具－>加载应用程序 中找到本程序后加载即 可! 成功加载后，在命令行内输入bzjgj即可运行。在 对话框中,可以选择螺栓螺母的类型以及其它平、弹垫、 螺纹的显示等，具体使用方法可参见本文件夹中的图片。 体验版只能绘制规格为M10之组件,如果您要注册本工 具,可在命令行内输入 zc ,即可获得相关注册信息。(Please support all the compressed files in AutoCAD package under the Held under the directory, such as AutoCAD installation directory or pull-down Menu Tools-> Options-> File-> Support file search path Path-> add the program directory. Transferred to the application jgj.vlx, This program can be directly dragged into the AutoCAD drawing area, or in the drop-down Menu Tools-> Load application found that after loading the program Can! After successfully loaded in the command line, type bzjgj to run. In Dialog box, you can select the type of bolts and nuts and other flat, spring washers, Threaded display, specific methods can be found in the folder of pictures. Trial version can only draw specifications for the components of the M10, if you want to register the workers Tools, you can enter in the command line zc, you can get the relevant registration information.)
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    2021-04-18 14:38:52下载
    积分：1
  • ghostimaging
    说明：  学习鬼成像原始程序。经验证可以跑通，欢迎交流。(Learn the original program of ghost imaging. After verification, it can run through. Welcome to communicate.)
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    说明：  该方法在传统的极化SAR滤波Lee方法上增加了自适应窗口处理以及滤波参数预估计等步骤，有效的提高了传统的滤波方法的抑制相干斑的能力，同时能够很好的保持极化SAR图像的极化散射特性。(in the traditional method of increasing polarization SAR Lee adaptive filtering window processing and pre-filtering parameters estimation step, effectively improve the traditional filter method of inhibiting the ability to spot coherent while being able to maintain a good polarization scattering characteristics of SAR images.)
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