Description: 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. Platform: |
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Author:kunkun |
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Description: 压缩传感理论仿真的一个实际例子，稀疏信号的恢复。-Compressed sensing theory of simulation of a practical example of sparse signal recovery. Platform: |
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Author:tanqinlin |
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Description: 详细介绍了图像稀疏分解思想在数据修复方面的应用。给出了较为详细的理论依据，以及简单的实例介绍-Details of the image sparse decomposition ideas in the application of data recovery. Gives a more detailed theoretical basis, as well as a simple example to illustrate Platform: |
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Author:chenlei |
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Description: 稀疏和低秩矩阵分解。
This paper focuses on the algorithmic improvement for the sparse and low-rank recovery.- Sparse and Low-Rank Matrix Decomposition Via Alternating Direction Methods.The problem of recovering the sparse and low-rank components of a matrix captures a broad spectrum of applications.
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Author:飒飒 |
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Description: 基于bregman算法在一维、二维、三维信号处理中的应用matlab工具箱-This toolbox provides the source code associated with the Bregman Cookbook
Doc:
- BregmanCookbook.pdf
In 1D:
-L1_SplitBregmanIteration.m : performs the recovery of a sparse signal affected by a known linear operator
In 2D:
-AddCurveletArray.m : sum the curvelet coefficients of two decomposition structures
-AddFrameletArray.m : sum the framelet coefficients of two decomposition structures
-ATV_NB_Deconvolution.m : performs the Nonblind Anisotropic Total Variation Deconvolution
-ATV_ROF.m : performs the Anisotropic Total Variation Denoising
-ITV_ROF.m : performs the Isotropic Total Variation Denoising
-Curvelet_NB_Deconvolution.m : performs the Nonblind Deconvolution based on Curvelet sparsity
-Framelet_NB_Deconvolution.m : performs the Nonblind Deconvolution based on Framelet sparsity (Analysis approach)
-Framelet_NB_Deconvolution2.m : performs the Nonblind Deconvolution based on Framelet sparsity (Synthesis approach)
-ShrinkCurvelet.m : performs the shrinkage of cu Platform: |
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Author:郑成勇 |
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Description: 将压缩感知用于谱估计中，根据论文谱压缩感知的一些程序-Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse
and compressible signals based on randomized dimensionality reduction. To recover a signal from its
compressive measurements, standard CS algorithms seek the sparsest signal in some discrete basis or
frame that agrees with the measurements. A great many applications feature smooth or modulated signals
that are frequency sparse and can be modeled as a superposition of a small number of sinusoids.
Unfortunately, such signals are only sparse in the discrete Fourier transform (DFT) domain when the
sinusoid frequencies live precisely at the center of the DFT bins. When this is not the case, CS recovery
performance degrades significantly. In this paper, we introduce a suite of spectral CS (SCS) recovery
algorithms for arbitrary frequency sparse signals. The key ingredients are an over-sampled DFT frame, a
signal model that inhibits closely spaced sinusoids, and classical sinusoid parameter e Platform: |
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Author:刘娟娟 |
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Description: 为提高压缩感知重构精度，该文提出一种分段弱阈值修正共轭梯度追踪算法。该算法修正了方向追踪算法
的方向，明确给出了搜寻原子下标的停止迭代准则，利用搜寻所得下标集通过最小二乘法得到稀疏信号的估计值。
仿真结果表明在同等稀疏的条件下实现精确重构，该算法与匹配追踪(MP)算法和分段正交匹配追踪FDR 阈值算
法(StOMP-FDR)相比,所需的观测值个数少20 ；在处理2 维图像信号时，其重构精度比分段正交匹配追踪FAR
阈值算法(StOMP-FAR)和贝叶斯算法(BCS)高1 。-In order to improve recovery accuracy for compressed sensing, a Stagewise Weak selection Modifying
approximation Conjugate Gradient Pursuit (StWMCGP) algorithm is proposed in this paper. This algorithm
modifies the direction in the directional pursuit algorithm and clearly presents a stopping criterion to search the
indices of elements and get a set. Then the evaluation of sparse signal is obtained by using Least-squares algorithm
and the set. Simulated results show that for the same sparsity level, the number of measurements needed by the
algorithm is about 20 less than that needed by MP or StOMP-FDR to exactly recover. When recovering
two-dimensional image signal, the recovery accuracy of this algorithm is about 1 higher than that of BCS or
StOMP-FAR. Platform: |
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Author:wang |
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Description: 该代码实现的是压缩感知理论中的信号恢复问题。将压缩感知理论中的信号恢复问题转化为带参数约束的回归问题，从而利用贝叶斯理论实现参数估计，从而得到高效的重建稀疏信号。-The code to achieve the signal recovery problems in the theory of compressed sensing. Recovery issues into regression problems with parameter constraints will signal compression perception theory, Bayesian theory parameter estimation, resulting in efficient reconstruction of sparse signal. Platform: |
Size: 2048 |
Author:Xu,J.P |
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Description: 关于不完整及不精确矩阵恢复的程序。输入矩阵的稀疏系数、测度矩阵、残缺矩阵和逼近容忍程度即可大概恢复出原矩阵并给出恢复评估系数。-Incomplete and inaccurate matrix recovery program. Sparse input matrix coefficients measure matrix, incomplete matrix approximation tolerance level you can probably recover the original matrix and gives the coefficient of recovery assessment. Platform: |
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Author:杨锦睿 |
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Description: Solve the standard basis pursuit program using a primal-dual algorithm,The key code of GBP is provided by Justin Romberg Reference: E. Candes and J. Romberg, “l1-Magic: Recovery of Sparse
Signals via Convex Programming,” 2005. Platform: |
Size: 4096 |
Author:sunmerzheng |
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Description: Compressive sampling is an emerging technique that promises to effectively recover a sparse signal from far fewer measurements than its dimension. The compressive sampling theory assures almost an exact recovery of a sparse signal if the signal is sensed randomly where the number of the measurements taken is proportional to the sparsity level and a log factor of the signal dimension. Encouraged by this emerging technique, this thesis briefly reviews the application of Compressive sampling in speech processing. It comprises the basic study of two necessary condition of compressive sensing theory: sparsity and incoherence. In this thesis, various sparsity domain and sensing matrix for speech signal and different pairs that satisfy incoherence condition has been compiled.-Compressive sampling is an emerging technique that promises to effectively recover a sparse signal from far fewer measurements than its dimension. The compressive sampling theory assures almost an exact recovery of a sparse signal if the signal is sensed randomly where the number of the measurements taken is proportional to the sparsity level and a log factor of the signal dimension. Encouraged by this emerging technique, this thesis briefly reviews the application of Compressive sampling in speech processing. It comprises the basic study of two necessary condition of compressive sensing theory: sparsity and incoherence. In this thesis, various sparsity domain and sensing matrix for speech signal and different pairs that satisfy incoherence condition has been compiled. Platform: |
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Author:Anuj |
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Description: 在这篇文章中,我们考虑问题的稀疏恢复从非线性测量,已应用于电网状态估计和不良数据检测。-In this article, we consider the problem of sparse recovery from nonlinear measurements, has been applied to the grid state estimation and bad data detection. Platform: |
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Author:kobe |
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Description: 针对压缩感知中的off-grid问题的稀疏自校正算法，参考文献“Sparse Frequency diverse MIMO radar imaging for Off-Grid target based on adaptive iterative MAP”-A novel approach of sparse adaptive calibration recovery via iterative maximum a posteriori (SACR-iMAP) for the general off-grid radar imaging。
reference“Sparse Frequency diverse MIMO radar imaging for Off-Grid target based on adaptive iterative MAP” Platform: |
Size: 1024 |
Author:kentL |
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Search - Recovery of Sparse