搜索资源列表
mp
- 改进的信号匹配追踪稀疏分解代码，基于gabor时频原子，对语音信号重构效果好-Improved matching pursuit signal decomposition sparse code, based on time-frequency gabor atom, for better voice signal reconstruction
dct_cs
- DCT压缩感知方案，采用DCT基稀疏分解原始信号，并且重构出原始信号-DCT compressed sensing scheme, using sparse decomposition of the original DCT-based signal and reconstruct the original signal
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 sens
aairomp
- 基于压缩传感CS的经典重构算法:正交匹配追踪OMP，能很好的重构稀疏信号。-Compressed sensing based on the classic CS reconstruction algorithms: orthogonal matching pursuit OMP, the reconstruction of sparse signals is well 朗读显示对应的拉丁字符的拼音 字典 翻译以下任意网
cs-code
- 一个正弦波利用DCT，FFT变换后稀疏化，然后应用压缩感知实现压缩，并有仿真图例说明重构效果。重构算法采用线性规划和OMP算法等，是一个初学CS入门的好例子。-A sine wave using DCT, FFT transform sparse, and then apply compressed sensing to achieve compression, and a legend reconstruction simulatio
CS
- 用matlab利用压缩感知CS实现对一位信号的处理~小波稀疏分解，正交追踪算法重构~1-D信号压缩传感的实现(正交匹配追踪法Orthogonal Matching Pursuit) 测量数M>=K*log(N/K),K是稀疏度,N信号长度,可以近乎完全重构-CS with matlab using compressed sensing to achieve a sparse signal processing- wavele
lectures-about-CS-and-SpaRec
- 一些关于压缩传感的基础性、系统性的介绍和一些稀疏信号重构算法的介绍如FOCUSS和Greedy Algorithm,适合入门人学习的资料-Some basis and system lectures about compressive sensing also including some sparse signal reconstruction algorithm for you such as FOCUSS and Greedy M
CS
- Matlab编写的压缩感知的库函数，包括稀疏分解和重构。-Written in compressed sensing Matlab library functions, including the sparse decomposition and reconstruction.
CS_Primary_tutorial
- CS压缩传感的初级教学代码，使用OMP重构，已注释，包括1维信号，2维图像的重构，分别使用dct和小波稀疏，列扫描和分块法进行omp重构-CS compressed sensing primary teaching code using OMP remodeling, already commented, including a 1-dimensional signals, 2-dimensional image reconstruct
CS-based-on-FFT-or-DWT
- 图像压缩感知,稀疏基为FFT或DWT（其中FFT是构造正交变换矩阵，DWT是对高频系数进行测量）；重构方法为OMP-compression sensing of image based on FFT or DWT
WT-OMPmatrix
- 对图像进行压缩感知，通过构造小波正交变换矩阵进行稀疏表示，用OMP重构-CS of image based on WT
cs-speech-enhancement
- 文利用带噪语音经特征基函数矩阵转换后所具有的稀疏特性，用最大似然估计方法对转换后得到的稀疏 分量进行非线性压缩去噪，然后再经过反变换和重构恢复出原始语音信号的估计。特征基函数矩阵反映了语音数据本 身的统计特性，因此具有很好的合理性和可取性。仿真结果表明利用稀疏编码方法能极大程度地抑制背景噪卢，与小波消噪法相比优势明显。-a speech enhancement algorithm based Compressed Sensing
CS-Code
- 压缩感知是新兴起来的一门重要学科，这里提供香港大学沙威最经典简单的一个框架。由小波先进行稀疏化，再用OMP算法进行修复重构-Compressed sensing is a new up an important subject, here Javert University of Hong Kong' s most classic of a simple fr a mework. First performed by the wa
Agorlitm compare
- 这是压缩感知贪婪重构算法中的各种算法编写，以及曲线拟合，分别从稀疏度和测量次数与重构率之间的关系进行的曲线拟合(This is a compilation of algorithms for compressed sensing, greedy reconstruction algorithms, and curve fitting. The curves are fitted from the relationship between
CS
- 采用DCS-SOMP算法对宽频信号进行重构 L1-SVD算法对低信噪比下的信号进行重构(Compressed sensing DCS-SOMP algorithm is used to reconstruct wideband signals L1-SVD algorithm for low SNR signal reconstruction)
cs-code
- 压缩感知稀疏重构算法。四种重构算法。。算法对比(Compressed sensing sparse reconstruction algorithm. Four reconstruction algorithms.)
cs
- 该文包含了压缩感知图像重构算法，有omp,cosamp,sp,可以选择观测矩阵高斯随机矩阵，稀疏随机矩阵，部分哈达码矩阵。(This paper includes compressed sensing image reconstruction algorithm. It has OMP, CoSaMP and sp. It can choose observation matrix Gauss random matrix, sparse
50825399CS_recovery_algorithms
- 多种稀疏重构方法文献，以及仿真结果，，，，(Literature on various sparse reconstruction methods and simulation results)
CS_Lenabmp
- BCS代码稀疏重构Lena.bmp图像，包中含有代码和图片本身，题主给了一定注释，便于CS初学者学习(BCS code sparsely reconstructs Lena. BMP image. The package contains code and image itself. The theme is given a certain comment, which is convenient for CS beginners to