CS
代码说明:
说明: 关于压缩感知重构算法, 压缩感知(Compressive Sensing, or Compressed Sampling,简称CS),是近几年流行起来的一个介于数学和信息科学的新方向,由Candes、Terres Tao等人提出,挑战传统的采样编码技术,即Nyquist采样定理。(Reconstruction algorithm on compressed sensing, compressive sensing (Compressive Sensing, or Compressed Sampling, referred to as CS), is popular in recent years, a range of mathematics and information science a new direction, by Candes, Terres Tao et al, challenge the traditional sample coding, that Nyquist sampling theorem.)
文件列表:
CS程序
......\aconv.m,860,2006-07-30
......\alpha0.m,1082,2010-05-10
......\block.m,424,2010-04-18
......\CS_chonggou.m,1218,2011-04-02
......\CS_mse.asv,797,2011-04-02
......\CS_mse.m,798,2011-04-02
......\DownDyadHi.m,606,2006-07-30
......\DownDyadLo.m,585,2006-07-30
......\dyad.m,469,2006-07-30
......\dyadlength.m,716,2006-07-30
......\fdrthresh.m,525,2006-07-30
......\FWT_PO.m,993,2006-03-28
......\GenFig11.m,1393,2010-04-22
......\iconv.m,777,2006-03-28
......\IWT_PO.m,938,2006-03-28
......\LockAxes.m,721,2006-07-30
......\lshift.m,466,2006-07-30
......\MakeBlocks.m,446,2006-03-28
......\MakeONFilter.m,10081,2006-07-30
......\MatrixEnsemble.m,3665,2006-04-10
......\MirrorFilt.m,591,2006-07-30
......\mse.m,1659,2010-04-22
......\NoiseMaker.m,890,2006-07-30
......\pdco.m,54596,2006-07-30
......\pdcoSet.m,10186,2006-07-30
......\PlotSpikes.m,750,2006-07-30
......\PlotWaveCoeff.m,944,2006-07-30
......\reverse.m,475,2006-07-30
......\rshift.m,479,2006-07-30
......\ShapeAsRow.m,480,2006-07-30
......\ShapeLike.m,963,2006-07-30
......\shijun.m,1916,2010-05-10
......\shuju.txt,41790,2010-05-12
......\shuju3.txt,77848,2010-05-24
......\shujumtm.txt,41322,2010-05-17
......\SolveBP.m,4483,2007-08-18
......\SolveMP.m,3942,2006-07-30
......\SolveOMP.m,5451,2006-07-30
......\SolveStOMP.m,4889,2006-07-30
......\StOMP_duibi.m,1622,2010-11-25
......\tangle.m,106,2010-04-18
......\twonorm.m,311,2006-07-30
......\UnlockAxes.m,599,2006-07-30
......\UpDyadHi.m,583,2006-07-30
......\UpDyadLo.m,558,2006-07-30
......\UpSample.m,637,2006-07-30
下载说明:请别用迅雷下载,失败请重下,重下不扣分!