搜索资源列表
A-REMARK-ON-COMPRESSED-SENSING
- 一篇关于压缩感知的经典文章，压缩感知（Compressed sensing，简称CS，也称为Compressive sampling)理论异于近代奈奎斯特采样定理，它指出：利用随机观测矩阵可以把一个稀疏或可压缩的高维信号投影到低维空间上，然后再利用这些少量的投影通过解一个优化问题就可以以高概率重构原始稀疏信号，并且证明了这样的随机投影包含了原始稀疏信号的足够信息。-A classic article on compressed sensing, compressive sensing (Comp
25-8x8-LED
- 基于CS-51单片机实现的LED点阵实例小程序，实现简单数字显示-Based on CS-51 Microcontroller LED dot matrix applet instance, simple figures
kmeans
- function [L,C] = kmeans(X,k) KMEANS Cluster multivariate data using the k-means++ algorithm. [L,C] = kmeans(X,k) produces a 1-by-size(X,2) vector L with one class label per column in X and a size(X,1)-by-k matrix C containing the centers
CS_OMP
- 使用OMP的CS重构算法，包含有lena图像。重构生成的图像质量由随机生成的重构矩阵决定-The use of OMP CS reconstruction algorithm, contains Lena image. Reconstruction image quality by the random generation of reconstruction matrix decision
Wavelet_IRLS
- 压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为ILRS算法，对256*256的lena图处理，比较原图和IRLS算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间-Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix
Wavelet_OMP
- 压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为OMP算法，对256*256的lena图处理，比较原图和OMP算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间 -Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix
Wavelet_SP
- 压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为SP算法，对256*256的lena图处理，比较原图和SP算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间-Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix and
Wavelet_ROMP
- 压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为ROMP算法，对256*256的lena图处理，比较原图和ROMP算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间 -Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matr
Wavelet_SL0
- 压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为SL0算法，对256*256的lena图处理，比较原图和SL0算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间 -Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix
files
- 压缩感知的很简单的入门小例子，基矩阵为正弦基，能很好地重构出稀疏信号-A simple example for the introduction of CS theory, the basis matrix is sinosoidal matrix, which can fully reconstruct the sparse signal.
LGME
- input: param: parameters of the LMGE algorithm param.mu, param.alpha, param.beta are regularization parameters. param.p: dimension of shared subspace param.k: number of nearest neighbors for Laplacian matrix X: input data Y: ground
ExtractBackground
- he files in this package comprise the Matlab implementation of a foreground segmentation algorithm based upon graph cuts, described in: Better Foreground Segmentation Through Graph Cuts, N. Howe & A. Deschamps. Tech report, http://arxiv.org/
P3
- 本程序可以绘制p3曲线，直接输入cv、cs、EX,也通过降雨或径流数据计算绘制 [cs,cv,ma] = p3plot(a,kk,b,bb,y1,cs_cv,ma,cs,cv,x0) a=20 横向网格条数 kk=200 纵坐标标注间隔（间隔要是最大值和最小值差的整数倍） b=2000 纵坐标最大值 bb=0 纵坐标最小值 y1=[] 降雨量，横向矩阵输入,如果没有请输入[] cs_cv=[2] 没有，请输入[] ma=[] 年平均降雨量，没有输
cgsvd
- CGSVD Compact generalized SVD of a matrix pair in regularization problems. sm = cgsvd(A,L) [U,sm,X,V] = cgsvd(A,L) , sm = [sigma,mu] Computes the generalized SVD of the matrix pair (A,L): [ A ] = [ U 0 ]*[ diag(sigma) 0 ]*inv
RanMatrix_CS
- 利用移位寄存器的原理生成测量矩阵，用于图像的压缩感知。可以避免传递过大的测量矩阵。-a kind of CS using a apecial sensing Matrix
sons
- Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear mod
sreenivas2009-icassp
- Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of