首页 课程资源 讲义     /    稀疏分解图像去噪

稀疏分解图像去噪

上传者: qq_24599599 | 上传时间:2022/9/4 0:28:02 | 文件大小:2.07MB | 文件类型:zip
稀疏分解图像去噪
传统的去噪方法往往假设含噪图像的有用信息处在低频区域,而噪声信息处在高频区域,从而基于中值滤波、Wiener滤波、小波变换等方法实现图像去噪,而实际上这种假设并不总是成立的。
基于图像的稀疏表示,近几年来研讨者们提出了基于过完备字典稀疏表示的图像去噪模型,其基本原理是将图像的稀疏表示作为有用信息,将逼近残差视为噪声。
利用K-SVD算法求得基于稀疏和冗余的训练字典,同时针对K-SVD算法仅适合处理小规模数据的局限,通过定义全局最优来强制图像局部块的稀疏性。
文献[28]提出了稀疏性正则化的图像泊松去噪算法,该算法采用log的泊松似然函数作为保真项,用图像在冗余字典下稀疏性约束作为正则项,从而取得更好的去噪效果。

文件下载

资源详情

[{"title":"(31个子文件2.07MB)稀疏分解图像去噪","children":[{"title":"稀疏分解图像去噪","children":[{"title":"Codes","children":[{"title":"csnr.m <span style='color:#111;'>501B</span>","children":null,"spread":false},{"title":"ParSet.m <span style='color:#111;'>469B</span>","children":null,"spread":false},{"title":"NonPeriodical_Simulated.m <span style='color:#111;'>381B</span>","children":null,"spread":false},{"title":"Utilize","children":[{"title":"SGE","children":[{"title":"SGE_Demo.m <span style='color:#111;'>1.31KB</span>","children":null,"spread":false},{"title":"SGEdestripe.m <span style='color:#111;'>731B</span>","children":null,"spread":false}],"spread":true},{"title":"TV","children":[{"title":"TVdestripe.m <span style='color:#111;'>1.86KB</span>","children":null,"spread":false},{"title":"TV_Demon.m <span style='color:#111;'>659B</span>","children":null,"spread":false}],"spread":true},{"title":"MomentMatching","children":[{"title":"MeanDN.m <span style='color:#111;'>185B</span>","children":null,"spread":false},{"title":"Moment_matching.m <span style='color:#111;'>1.84KB</span>","children":null,"spread":false}],"spread":true},{"title":"UTV","children":[{"title":"UTV_Demon.m <span style='color:#111;'>477B</span>","children":null,"spread":false},{"title":"shrink.m <span style='color:#111;'>238B</span>","children":null,"spread":false},{"title":"UTVdestripe.m <span style='color:#111;'>1.91KB</span>","children":null,"spread":false}],"spread":true},{"title":"WFAF","children":[{"title":"adpative_FFT.m <span style='color:#111;'>1.18KB</span>","children":null,"spread":false},{"title":"WFAF.m <span style='color:#111;'>1.12KB</span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"power_spectrum.m <span style='color:#111;'>399B</span>","children":null,"spread":false},{"title":"Periodical_Simulated.m <span style='color:#111;'>510B</span>","children":null,"spread":false},{"title":"SVD_shrink.m <span style='color:#111;'>927B</span>","children":null,"spread":false},{"title":"MILR_destripe.m <span style='color:#111;'>1.94KB</span>","children":null,"spread":false},{"title":"phiprimeover2x.m <span style='color:#111;'>680B</span>","children":null,"spread":false},{"title":"MParSet.m <span style='color:#111;'>547B</span>","children":null,"spread":false},{"title":"cal_ssim.m <span style='color:#111;'>6.22KB</span>","children":null,"spread":false},{"title":"pouxiantu.m <span style='color:#111;'>386B</span>","children":null,"spread":false},{"title":"periodo.m <span style='color:#111;'>343B</span>","children":null,"spread":false},{"title":"SILR_destripe.m <span style='color:#111;'>2.00KB</span>","children":null,"spread":false},{"title":"soft_shrink.m <span style='color:#111;'>242B</span>","children":null,"spread":false}],"spread":false},{"title":"test_destripe.m <span style='color:#111;'>1.05KB</span>","children":null,"spread":false},{"title":"Images","children":[{"title":"Original_band30.tif <span style='color:#111;'>92.59KB</span>","children":null,"spread":false},{"title":"Terra33_fig8.tif <span style='color:#111;'>254.54KB</span>","children":null,"spread":false},{"title":"multi-images.mat <span style='color:#111;'>1.57MB</span>","children":null,"spread":false},{"title":"ir_background.png <span style='color:#111;'>49.20KB</span>","children":null,"spread":false},{"title":"Terraband30.tif <span style='color:#111;'>258.19KB</span>","children":null,"spread":false}],"spread":true}],"spread":true}],"spread":true}]

评论信息

  • 明道士:
    可以运行的2019-05-09

免责申明

【好快吧下载】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,【好快吧下载】 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 【好快吧下载】 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,8686821#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