搜索资源列表
dissert
- Sparse Signal Representation using Overlapping Frames-Sparse Signal Representation using Overl apping Frames
FrameTools
- Sparse Signal Representation using Overlapping Frames (matlab toolbox)-Sparse Signal Representation using Overl apping Frames (Matlab toolbox)
GreedyBasisPursuit
- a new algorithm for computing sparse signal representations using overcomplete dictionaries
sparserepresentationofsignals.
- 详细介绍了信号的稀疏分解和表示方法，可以用于图像特征提取等方面,Details of the sparse signal decomposition and that the method can be used for image feature extraction, etc.
cs.rar
- 压缩传感理论的一个简单例子，首先对信号进行稀疏采样，然后利用MP算法对信号进行重建。,Compressed sensing theory of a simple example, first of all, the signal sparse sampling, and then use MP algorithm of signal reconstruction.
CoSaMP
- 压缩感知中压缩采样匹配追踪算法，用于稀疏信号的重构-Compressed sensing algorithm in the compressed sample matching pursuit for sparse signal reconstruction
A-Sparse-Learning-Package
- 美国莱斯大学压缩感知稀疏学习工具箱，能把信号进行稀疏表示，进而实现远低于奈奎斯特抽样速率的压缩感知-Rice University study Toolbox sparse compressed sensing, sparse representation of the signal can, thus achieving much lower than the Nyquist sampling rate of the compressed sensing
Professor-Lu-Wusheng-lecture
- 陆吾生教授是加拿大维多利亚大学电气与计算机工程系的教授。此课件为其在国内大学短期精品课程的课件。包含最优化问题求解，压缩感知方法及其在稀疏信号和图像处理中的应用（压缩、重构、降噪等）。-Professor Lu Wusheng University of Victoria, Canada Professor of Electrical and Computer Engineering. The courseware for the University in the domestic short
SolveBP
- BP，基追踪，同MP一样，是实现信号稀疏分解的方法-BP, basis pursuit, as with the MP, is to achieve sparse signal decomposition method
DCS_spectrum_sensing
- 分布式压缩感知，DCS_SOMP算法。用于稀疏信号的分布式恢复。-Distributed compressed sensing, DCS_SOMP algorithm. Distributed for sparse signal recovery.
signal_decomposition_MP
- 稀疏信号分解利用匹配追踪算法，主程序+调用函数-Sparse signal decomposition, the main program calls the function+
sparsify_0_3
- sparsify is a set of Matlab m-files implementing a range of different algorithms to calculate sparse signal approximations. Currently sparsify contains two main sets of algorithms, greedy methods (collected under the name of GreedLab) and hard thresh
malioutov_MS_thesis
- 应用稀疏信号重组法来进行的传感器阵列声源定位。是MIT的Dmitry M. Malioutov的博士毕业论文。-A Sparse Signal Reconstruction Perspective for Source Localization with Sensor Arrays_by Dmitry M. Malioutov
FrameEx
- Sparse signal representation using overlapping frames论文的实现-Sparse signal representation using overlapping frames
ksvdbox12
- 采用KSVD算法通过训练的方法来构造稀疏过完备字典，在使用时一定要确保已装有ompbox9。可用于语音，图像信号处理等的稀疏字典构造-KSVD algorithm using the method of training to construct the sparse over-complete dictionary, in use, make sure have been installed ompbox9. Can be used for the sparse dictionary cons
Sparse-and-Redundant-Representations
- Sparse and Redundant Representations From Theory to Applications in Signal and Image Processing，稀疏表示的最新巨作-Sparse and Redundant Representations From Theory to Applications in Signal and Image Processing, sparse representation of the latest blockbuster
Sparse-Signal-Reconstruction-
- 稀疏信号重构的远景分析与传感器信源定位综述分析 -A Sparse Signal Reconstruction Perspective for Source Localization With Sensor Arrays
A-Robust-Algorithm-for-Joint-Sparse
- 脉冲噪声背景下的联合稀疏恢复方法， 在不同背景下给出了测试结果-presents a robust solution for joint sparse recovery (JSR) under impulsive noise. The unknown measurement noise is endowed with the Student-t distribution, then a novel Bayesian probabilistic model is proposed to
Sparse image and signal processing
- 这本书在稀疏的多尺度图像和信号处理提出了艺术状态，包括线性多尺度变换，如小波，脊波和曲波变换、非线性、多尺度变换基于中值和数学形态学算子。最近的稀疏性和形态多样性的概念描述和利用各种问题，如去噪，反问题正规化，稀疏信号分解，盲源分离，压缩感知。 这本书的理论和实践研究相结合的领域，如天文学、生物学、物理学、数字媒体应用和取证。最后一章探讨了信号处理中的一个范式转换，表明以前的信息取样和提取的限制可以用非常重要的方法加以克服。 MATLAB和IDL代码伴随这些方法和应用程序重现。 实验并说明
orthogonal least squares
- The following Matlab project contains the source code and Matlab examples used for orthogonal least squares algorithms for sparse signal reconstruction. Added after previous version ols_gp: Sparse reconstruction by Orthogonal Least Squares followed b