MATLAB实现k-svd和mod信号处理

上传者: 42897786 | 上传时间: 2019-12-21 21:47:00 | 文件大小: 5.74MB | 文件类型: rar
包含K-SVD和MOD两种算法对信号和图像处理代码例子。使用DCT字典,使用OMP算法计算稀疏系数。

文件下载

资源详情

[{"title":"( 24 个子文件 5.74MB ) MATLAB实现k-svd和mod信号处理","children":[{"title":"KSVD","children":[{"title":"~WRL1849.tmp <span style='color:#111;'> 15.24KB </span>","children":null,"spread":false},{"title":"demo3.m <span style='color:#111;'> 8.30KB </span>","children":null,"spread":false},{"title":"MOD.m <span style='color:#111;'> 7.86KB </span>","children":null,"spread":false},{"title":"~$表示KSVD.docx <span style='color:#111;'> 162B </span>","children":null,"spread":false},{"title":"gererateSyntheticDictionaryAndData.m <span style='color:#111;'> 1.85KB </span>","children":null,"spread":false},{"title":"demo2.m <span style='color:#111;'> 3.51KB </span>","children":null,"spread":false},{"title":"denoiseImageKSVD.m <span style='color:#111;'> 10.20KB </span>","children":null,"spread":false},{"title":"denoiseImageDCT.m <span style='color:#111;'> 6.68KB </span>","children":null,"spread":false},{"title":"displayDictionaryElementsAsImage.m <span style='color:#111;'> 4.24KB </span>","children":null,"spread":false},{"title":"DCT_test.m <span style='color:#111;'> 218B </span>","children":null,"spread":false},{"title":"allkindsofmethodscompare.m <span style='color:#111;'> 3.69KB </span>","children":null,"spread":false},{"title":"稀疏表示KSVD.docx <span style='color:#111;'> 113.76KB </span>","children":null,"spread":false},{"title":"demo1.m <span style='color:#111;'> 1.86KB </span>","children":null,"spread":false},{"title":"w.jpg <span style='color:#111;'> 21.88KB </span>","children":null,"spread":false},{"title":"my_im2col.m <span style='color:#111;'> 857B </span>","children":null,"spread":false},{"title":"lena.bmp <span style='color:#111;'> 257.05KB </span>","children":null,"spread":false},{"title":"KSVD.m <span style='color:#111;'> 16.04KB </span>","children":null,"spread":false},{"title":"OMPerr.m <span style='color:#111;'> 1.34KB </span>","children":null,"spread":false},{"title":"KSVD_NN.m <span style='color:#111;'> 11.31KB </span>","children":null,"spread":false},{"title":"denoiseImageGlobal.m <span style='color:#111;'> 6.41KB </span>","children":null,"spread":false},{"title":"BJ200_14.BMP <span style='color:#111;'> 40.12KB </span>","children":null,"spread":false},{"title":"NN_BP.m <span style='color:#111;'> 1.08KB </span>","children":null,"spread":false},{"title":"globalTrainedDictionary.mat <span style='color:#111;'> 5.48MB </span>","children":null,"spread":false},{"title":"OMP.m <span style='color:#111;'> 954B </span>","children":null,"spread":false}],"spread":false}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明