搜索资源列表
RIPText
- 用来测试压缩感知中构造的测量矩阵的RIP的-this .m file is used to check the senging matrix RIP in CS
Matrix-cs
- 矩阵类，可进行矩阵的各种运算，并可求解Ｎ阶线性方程-matrices, matrix for the various operators and can solve linear equations N
liehuanweifa
- 行列换位法采用密钥，假设密钥是5，明文是I am a Chinese boy那么就会以5位列数排成一个矩阵。 I a m a c h I n e s e b o y 那么密文就是ihe aib mno aey cs× ，最后一行不足的用随机字母填充，（×代表那个随机字母）！-The ranks of the use of key transposition law, assuming that key is 5, is clearly I am a Chinese boy then
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
CSlearning
- CS理论里观测矩阵的优化算法以及训练稀疏字典的算法-optimization of measurement matrix and dictionary learning for sparse approximations
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
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/
image-CS
- 基于CS的图像压缩解压，包括CoSaMP和OMP恢复算法，离散小波变换矩阵构造，具体对图像进行压缩解压-CS-based image compression and decompression, including CoSaMP and OMP recovery algorithms, discrete wavelet transform matrix structure, specifically for image compression and decompression
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
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
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
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.)