基于Matlab的多图像超分辨率重建算法

上传者: jyong2012 | 上传时间: 2011-10-23 00:00:00 | 文件大小: 139KB | 文件类型: rar
多图像超分辨率的实现主要就是将具有相似而又不同却又互相补充信息的配准影像融到一起,得到非均匀采样的较高分辨率数据,复原需要亚像素精度的运动矢量场,然而它们之间的运动模型估计精确与否直接影响到重建的效果,因此影像配准和运动模型的估计精度是高分辨率图像重建的关键。由于实际中不同时刻获得的影像数据间存在较大的变形、缩放、旋转和平移,因此必须对其进行配准,在此基础上进行运动模型估计。然后通过频率域或空间域的重建处理,生成均匀采样的超分辨率数据

文件下载

资源详情

[{"title":"( 51 个子文件 139KB ) 基于Matlab的多图像超分辨率重建算法","children":[{"title":"superresolution_v_2.0","children":[{"title":"__MACOSX","children":[{"title":"superresolution_v_2.0","children":[{"title":"._readme.txt <span style='color:#111;'> 82B </span>","children":null,"spread":false},{"title":"._.DS_Store <span style='color:#111;'> 82B </span>","children":null,"spread":false},{"title":"application","children":[{"title":"html","children":[{"title":"._.DS_Store <span style='color:#111;'> 82B </span>","children":null,"spread":false}],"spread":true},{"title":"._.DS_Store <span style='color:#111;'> 82B </span>","children":null,"spread":false}],"spread":true}],"spread":true}],"spread":true},{"title":"superresolution_v_2.0","children":[{"title":".DS_Store <span style='color:#111;'> 6.00KB </span>","children":null,"spread":false},{"title":"readme.txt <span style='color:#111;'> 3.87KB </span>","children":null,"spread":false},{"title":"application","children":[{"title":"papoulisgerchberg.m <span style='color:#111;'> 3.02KB </span>","children":null,"spread":false},{"title":"robustSR.m <span style='color:#111;'> 3.70KB </span>","children":null,"spread":false},{"title":"g_im_LR_1.tif <span style='color:#111;'> 1.62KB </span>","children":null,"spread":false},{"title":"g_im_LR_2.tif <span style='color:#111;'> 1.61KB </span>","children":null,"spread":false},{"title":"superresolution.m <span style='color:#111;'> 63.87KB </span>","children":null,"spread":false},{"title":".DS_Store <span style='color:#111;'> 15.00KB </span>","children":null,"spread":false},{"title":"n_conv.m <span style='color:#111;'> 3.18KB </span>","children":null,"spread":false},{"title":"estimate_rotation.m <span style='color:#111;'> 3.37KB </span>","children":null,"spread":false},{"title":"keren.m <span style='color:#111;'> 3.95KB </span>","children":null,"spread":false},{"title":"estimate_motion.m <span style='color:#111;'> 1.63KB </span>","children":null,"spread":false},{"title":"marcel_shift.m <span style='color:#111;'> 2.14KB </span>","children":null,"spread":false},{"title":"applicability.m <span style='color:#111;'> 1.38KB </span>","children":null,"spread":false},{"title":"keren_shift.m <span style='color:#111;'> 2.69KB </span>","children":null,"spread":false},{"title":"wind_LR_3.tif <span style='color:#111;'> 4.34KB </span>","children":null,"spread":false},{"title":"estimate_shift.m <span style='color:#111;'> 2.42KB </span>","children":null,"spread":false},{"title":"superresolution.fig <span style='color:#111;'> 19.72KB </span>","children":null,"spread":false},{"title":"generation.fig <span style='color:#111;'> 4.48KB </span>","children":null,"spread":false},{"title":"SR_about.m <span style='color:#111;'> 644B </span>","children":null,"spread":false},{"title":"wind_LR_1.tif <span style='color:#111;'> 4.37KB </span>","children":null,"spread":false},{"title":"html","children":[{"title":".DS_Store <span style='color:#111;'> 6.00KB </span>","children":null,"spread":false},{"title":"SR_documentation.html <span style='color:#111;'> 25.80KB </span>","children":null,"spread":false},{"title":"SR_about.html <span style='color:#111;'> 3.82KB </span>","children":null,"spread":false}],"spread":false},{"title":"lucchese.m <span style='color:#111;'> 5.06KB </span>","children":null,"spread":false},{"title":"logo_epfl_small.tif <span style='color:#111;'> 42.10KB </span>","children":null,"spread":false},{"title":"wind_LR_2.tif <span style='color:#111;'> 4.37KB </span>","children":null,"spread":false},{"title":"logo_warning.tif <span style='color:#111;'> 146.58KB </span>","children":null,"spread":false},{"title":"g_im_LR_4.tif <span style='color:#111;'> 1.61KB </span>","children":null,"spread":false},{"title":"Contents.m <span style='color:#111;'> 4.83KB </span>","children":null,"spread":false},{"title":"interpolation.m <span style='color:#111;'> 3.07KB </span>","children":null,"spread":false},{"title":"c2p.m <span style='color:#111;'> 1.88KB </span>","children":null,"spread":false},{"title":"._html <span style='color:#111;'> 82B </span>","children":null,"spread":false},{"title":"generatePSF.m <span style='color:#111;'> 1.60KB </span>","children":null,"spread":false},{"title":"shift.m <span style='color:#111;'> 2.43KB </span>","children":null,"spread":false},{"title":"marcel.m <span style='color:#111;'> 3.02KB </span>","children":null,"spread":false},{"title":"iteratedbackprojection.m <span style='color:#111;'> 3.68KB </span>","children":null,"spread":false},{"title":"g_im_LR_3.tif <span style='color:#111;'> 1.61KB </span>","children":null,"spread":false},{"title":"pocs.m <span style='color:#111;'> 2.60KB </span>","children":null,"spread":false},{"title":"n_convolution.m <span style='color:#111;'> 15.09KB </span>","children":null,"spread":false},{"title":"create_images.m <span style='color:#111;'> 2.64KB </span>","children":null,"spread":false},{"title":"generation.m <span style='color:#111;'> 18.13KB </span>","children":null,"spread":false},{"title":"wind_LR_4.tif <span style='color:#111;'> 4.37KB </span>","children":null,"spread":false},{"title":"lowpass.m <span style='color:#111;'> 1.71KB </span>","children":null,"spread":false},{"title":"robustnorm2.m <span style='color:#111;'> 1.10KB </span>","children":null,"spread":false},{"title":"gpl <span style='color:#111;'> 14.79KB </span>","children":null,"spread":false},{"title":"SR_documentation.m <span style='color:#111;'> 10.84KB </span>","children":null,"spread":false}],"spread":false}],"spread":true}],"spread":true}],"spread":true}]

评论信息

  • weixin_40272419 :
    怎么运行???
    2018-02-28
  • clclcl99999 :
    只能用于新手用作demo练习
    2017-08-30
  • qq_29628853 :
    怎么运行啊?
    2017-06-27
  • _挨踢_ :
    终于可以在我的机器上跑了,但是效果一般。
    2017-03-30
  • Paul-Huang :
    效果不明显
    2016-07-06

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