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dissert
- Sparse Signal Representation using Overlapping Frames-Sparse Signal Representation using Overl apping Frames
Sparse_Signal_Representation
- 介绍压缩传感理论中要用到的信号的稀疏表征原理-Introduced the theory of compressed sensing to use the sparse representation of signal theory
Sparse-Coding-by-Elad
- Elad写的关于稀疏理论的书，内容丰富，适合初学稀疏理论的同学，不容错过额-a book about sparse representation of signal and its practice
system-identification
- 采用时频聚集性较好的线性调频信号作为线性时不变系统输入激励，采用Gabor字典作为过完备原子库。在利用传统系统辨识法之前先利用稀疏分解算法将输出信号进行去噪处理，显著提高系统辨识精度。 具体包括互谱算法，信号的Gabor稀疏分解的详细代码-Space can be a time for sparse decomposition to solve the problem of huge memory needed。This approach, combined with the rapid d
KSVD_Matlab_ToolBox
- 数字图像处理，K-SVD字典学习方法，信号的稀疏与冗余表示理论，图像压缩，图像去噪-Digital image processing, K-SVD dictionary learning methods, sparse and redundant signal representation theory, image compression, image denoising
Introduction-Compressed-Sensing
- 压缩感知（CS）理论是在已知信号具有稀疏性或可压缩性的条件下，对信号数据进行采集、 编解码的新理论。主要阐述了CS理论框架以及信号稀疏表示、CS编解码模型，并举例说明基于压缩感知理论的编解码理论在一维信号、二维图像处理上的应用。 -Compressed Sensing(CS) theory is a novel data collection and coding theory under the condition that signal is sparse or compress
Compressed-Sensing-Theory
- 用压缩感知理论对信号数据进行采集、编解码,进行数据恢复。主要阐述了CS理论框架以及信号稀疏表示、CS编解码模型.-Compressed Sensing(CS) theory is a novel data collection and coding theory under the condition that signal is sparse or compressible.
COOMP
- Cooperative Greedy Pursuit Strategies are considered for approximating a signal partition subjected to a global constraint on sparsity. The approach aims at producing a high quality sparse approximation of the whole signal, using highly coherent
Compressive-Sensing-for-Signal-Ensembles
- Compressive sensing (CS) is a new approach to simultaneous sensing and compression that enables a potentially large reduction in the sampling and computation costs for acquisition of signals having a sparse or compressible representation in some
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