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l1_cs
- 对lena.map先分块处理，然后做cs变换，观测矩阵用随机高斯矩阵，重构算法用l1算法-On lena.map first block processed, and then do cs transform, random Gaussian matrix with the observation matrix, reconstruction algorithm algorithm using l1
cs-matrix-of-measurement
- 文章是基于压缩感知理论的测量矩阵的研究。测量矩阵的选择是压缩感知理论的关键点，直接关系到信号重建效果的好坏！-Article is based on the theory of compressed sensing matrix measurement research. Measurement matrix of choice is the key to the theory of compressed sensing point, signal reconstruction is direc
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
basis-matrix
- 生成dct基矩阵，傅里叶基矩阵，可用于压缩感知的基矩阵等-generate dct basis matrix,forier matrix,twhich can be used for CS theory
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.
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/
CS-of-lfm-using-OMP
- 此仿真的原理基于压缩感知理论，以LFM为采集信号，测量矩阵使用的是伯努利矩阵，恢复算法采用的是OMP算法，恢复效果不错，想学习压缩感知方面知识的可以下载仿真使用！ -The principle of the simulation based on compressed sensing theory, collection for LFM signal, measure matrix using Bernoulli matrix, recovery algorithm USES is OMP
CS-Mtx
- 压缩感知中压缩测量的几种测量矩阵的构造，包括伯努利矩阵，循环矩阵和部分傅里叶矩阵等。(Several measurement matrices for compression measurement in compressed sensing are constructed, including Bernoulli matrix, cyclic matrix and partial Fourier matrix.)