登录
首页 » matlab » elleSR_0.2

elleSR_0.2

于 2021-03-01 发布 文件大小:496KB
0 103
下载积分: 1 下载次数: 345

代码说明:

  多帧图像超分辨率重建,里面有huber 马尔科夫,ML超分辨率重建,对学习超分辨率很有帮助。(Multi-frame image super-resolution reconstruction, which has the the huber Markov, ML super-resolution reconstruction is helpful in learning the super-resolution.)

文件列表:

elleSR_0.2
..........\elle_eval_huber.cpp,2165,2012-02-23
..........\elle_eval_huber.mexw32,8704,2012-03-24
..........\elle_eval_huber_grad.cpp,3214,2012-02-23
..........\elle_eval_huber_grad.mexw32,9216,2012-03-24
..........\getAvim.cpp,9529,2012-02-23
..........\getAvim.mexw32,10752,2012-03-24

..........\makeLR.cpp,8634,2012-02-23
..........\makeLR.mexw32,11776,2012-03-24
..........\makeW.cpp,12422,2012-02-23
..........\makeW.mexw32,11776,2012-03-24
..........\mex_amub.cpp,5219,2012-02-23
..........\mex_amub.mexw32,9728,2012-03-24
..........\netlab3_3
..........\.........\conffig.m,942,2004-06-18
..........\.........\confmat.m,1349,2004-06-18
..........\.........\conjgrad.m,4715,2004-06-18
..........\.........\consist.m,2607,2004-06-18
..........\.........\Contents.m,11172,2004-06-18
..........\.........\convertoldnet.m,724,2004-06-18
..........\.........\datread.m,2611,2004-06-18
..........\.........\datwrite.m,1141,2004-06-18
..........\.........\dem2ddat.m,1362,2004-06-18
..........\.........\demard.m,6175,2004-06-18
..........\.........\demev1.m,5511,2004-06-18
..........\.........\demev2.m,8201,2004-06-18
..........\.........\demev3.m,5718,2004-06-18
..........\.........\demgauss.m,2267,2004-06-18
..........\.........\demglm1.m,2449,2004-06-18
..........\.........\demglm2.m,2552,2004-06-18
..........\.........\demgmm1.m,6067,2004-06-18
..........\.........\demgmm2.m,4593,2004-06-18
..........\.........\demgmm3.m,5799,2004-06-18
..........\.........\demgmm4.m,6414,2004-06-18
..........\.........\demgmm5.m,4886,2004-06-18
..........\.........\demgp.m,4614,2004-06-18
..........\.........\demgpard.m,4167,2004-06-18
..........\.........\demgpot.m,1036,2004-06-18
..........\.........\demgtm1.m,4889,2004-06-18
..........\.........\demgtm2.m,5579,2004-06-18
..........\.........\demhint.m,1509,2004-06-18
..........\.........\demhmc1.m,4798,2004-06-18
..........\.........\demhmc2.m,4422,2004-06-18
..........\.........\demhmc3.m,4519,2004-06-18
..........\.........\demkmn1.m,3039,2004-06-18
..........\.........\demknn1.m,2451,2004-06-18
..........\.........\demmdn1.m,6290,2004-06-18
..........\.........\demmet1.m,2926,2004-06-18
..........\.........\demmlp1.m,2502,2004-06-18
..........\.........\demmlp2.m,9057,2004-06-18
..........\.........\demnlab.m,9505,2004-06-18
..........\.........\demns1.m,3397,2004-06-18
..........\.........\demolgd1.m,3722,2004-06-18
..........\.........\demopt1.m,6066,2004-06-18
..........\.........\dempot.m,487,2004-06-18
..........\.........\demprgp.m,16624,2004-06-18
..........\.........\demprior.m,11346,2004-06-18
..........\.........\demrbf1.m,4358,2004-06-18
..........\.........\demsom1.m,3457,2004-06-18
..........\.........\demtrain.m,9923,2004-06-18
..........\.........\dist2.m,891,2004-06-18
..........\.........\eigdec.m,1342,2004-06-18
..........\.........\errbayes.m,1275,2004-06-18
..........\.........\evidence.m,3497,2004-06-18
..........\.........\fevbayes.m,1932,2004-06-18
..........\.........\gauss.m,777,2004-06-18
..........\.........\gbayes.m,1560,2004-06-18
..........\.........\glm.m,2783,2004-06-18
..........\.........\glmderiv.m,1161,2004-06-18
..........\.........\glmerr.m,1474,2004-06-18
..........\.........\glmevfwd.m,994,2004-06-18
..........\.........\glmfwd.m,1775,2004-06-18
..........\.........\glmgrad.m,1067,2004-06-18
..........\.........\glmhess.m,4024,2004-06-18
..........\.........\glminit.m,1121,2004-06-18
..........\.........\glmpak.m,402,2004-06-18
..........\.........\glmtrain.m,6198,2004-06-18
..........\.........\glmunpak.m,819,2004-06-18
..........\.........\gmm.m,3978,2004-06-18
..........\.........\gmmactiv.m,2332,2004-06-18
..........\.........\gmmem.m,4997,2004-06-18
..........\.........\gmminit.m,3341,2004-06-18
..........\.........\gmmpak.m,527,2004-06-18
..........\.........\gmmpost.m,966,2004-06-18
..........\.........\gmmprob.m,658,2004-06-18
..........\.........\gmmsamp.m,1842,2004-06-18
..........\.........\gmmunpak.m,1770,2004-06-18
..........\.........\gp.m,3168,2004-06-18
..........\.........\gpcovar.m,1006,2004-06-18
..........\.........\gpcovarf.m,1125,2004-06-18
..........\.........\gpcovarp.m,840,2004-06-18
..........\.........\gperr.m,1421,2004-06-18
..........\.........\gpfwd.m,1576,2004-06-18
..........\.........\gpgrad.m,2585,2004-06-18
..........\.........\gpinit.m,1404,2004-06-18
..........\.........\gppak.m,480,2004-06-18
..........\.........\gpunpak.m,975,2004-06-18
..........\.........\gradchek.m,1822,2004-06-18
..........\.........\graddesc.m,5412,2004-06-18

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • matlabgongchengkongzhigongjuxiang
    详细介绍了MATLAB与控制工程相关的6个基础工具箱:系统辨识工具箱、控 制系统工具箱、鲁棒控制工具箱、模型预测控制工具箱、模糊逻辑工具箱和非线性控制设计模块。同时提供了MATLAB的一些基础知识。(Details related to MATLAB and Control Engineering of the six basic kit: System Identification Toolbox, Control System Toolbox, Robust Control Toolbox, Model Predictive Control Toolbox, Fuzzy Logic Toolbox and nonlinear control design module. Also provides some basic knowledge of MATLAB.)
    2011-08-27 11:57:41下载
    积分:1
  • 00806791
    SEMINAR FILE FROM IEEE TRANSATION IN COMPUTER SCIENCE AND INFORMATION TECHNOLOGY
    2013-10-04 18:16:47下载
    积分:1
  • GA
    说明:  Genetic Algorithm MATLAB Code for Unconstrained Optimization Problem
    2013-12-20 18:57:42下载
    积分:1
  • MyKmeans
    实现聚类K均值算法: K均值算法:给定类的个数K,将n个对象分到K个类中去,使得类内对象之间的相似性最大,而类之间的相似性最小。 缺点:产生类的大小相差不会很大,对于脏数据很敏感。 改进的算法:k—medoids 方法。这儿选取一个对象叫做mediod来代替上面的中心 的作用,这样的一个medoid就标识了这个类。步骤: 1,任意选取K个对象作为medoids(O1,O2,…Oi…Ok)。 以下是循环的: 2,将余下的对象分到各个类中去(根据与medoid最相近的原则); 3,对于每个类(Oi)中,顺序选取一个Or,计算用Or代替Oi后的消耗—E(Or)。选择E最小的那个Or来代替Oi。这样K个medoids就改变了,下面就再转到2。 4,这样循环直到K个medoids固定下来。 这种算法对于脏数据和异常数据不敏感,但计算量显然要比K均值要大,一般只适合小数据量。(achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n will be assigned to target K to 000 category, making target category of the similarity between the largest category of the similarity between the smallest. Disadvantages : class size have no great difference for dirty data is very sensitive. Improved algorithms : k-medoids methods. Here a selection of objects called mediod to replace the center of the above, the logo on a medoid this category. Steps : 1, arbitrary selection of objects as K medoids (O1, O2, Ok ... ... Oi). Following is a cycle : 2, the remaining targets assigned to each category (in accordance with the closest medoid principle); 3, for each category (Oi), the order of selection of a Or, calculated Oi Or replace the consumption-E (Or))
    2005-07-26 01:32:58下载
    积分:1
  • buck
    本仿真为电动汽车充电仿真,采用了非隔离式的DC/DC环节(The simulation for the electric car charging simulation, using a non-isolated DC/the DC link)
    2012-07-23 11:20:32下载
    积分:1
  • matlab-image-processing
    MATLAB数字图像处理书籍\matlab图像处理操作程序(matlab image processing procedures)
    2014-10-24 18:06:48下载
    积分:1
  • MVAsirdata
    MVAsirdata generates a data set and applies the sliced inverse regression algorithm (SIR) for dimension reduction.
    2013-04-30 22:31:13下载
    积分:1
  • H_7level
    7级级联H桥逆变器 多电平大功率电力电子 中高压无功补偿 变频器 matlab仿真模型(7 cascaded H-bridge inverter multi-level high-power electronic converter high voltage reactive power compensation)
    2014-03-07 09:35:35下载
    积分:1
  • tp4
    this file is a matlab program wich contain a filtering tests
    2011-11-17 21:19:40下载
    积分:1
  • and-gate
    programming of and gate
    2016-11-22 14:30:48下载
    积分:1
  • 696518资源总数
  • 104360会员总数
  • 40今日下载