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generate_graphs
- MIT稀疏傅里叶变换 小组的SFFT 源码： 运行的SFFT和FFTW参数和重复的范围内，两者的运行时间 诗句信号的大小（n）或稀疏（ K ） 。重新创建图表的文件： 稀疏傅里叶变换， SODA 12简单和实用的算法。 -Runs sFFT and FFTW for a range of parameters and plots the runtime of both verse the signal size (n) or the sparsity (k). Recreat
subspace_J21
- Input: X, training data, dim*num Y, training data labels, num*class para.alpha, para.beta, regularization parameters para.rd, reduced dimensionality Web Image Annotation via Subspace-Sparsity Collaborated Feature Selection. Zh
inpaint
- 基于压缩感知的图像修复,，基于图像在复数小波变换上的稀疏性，利用迭代硬阈值方法 求解重构模型，进而获得重构图像.-Based on compressed sensing image restoration, image-based complex wavelet transform on the sparsity of the iterative method for solving hard threshold reconstruction model, and then get the
3-3-1
- 基于图像的稀疏性用OMP算法对图像去噪，思路清晰，容易理解-Based on the sparsity of the image with OMP algorithm for image denoising, clear, easy to understand
modelcs_v1.1
- modelcs_v1.1 - 1D trees, 2D trees, Block sparsity, Clustered sparsity
LSL_SC
- based classification (SRC) has been widely used for face recognition (FR). SRC first codes a testing sample as a sparse linear combination of all the training samples, and then classifies the testing sample by evaluating which class leads to the mini
spgl1-1.8
- based classification (SRC) has been widely used for face recognition (FR). SRC first codes a testing sample as a sparse linear combination of all the training samples, and then classifies the testing sample by evaluating which class leads to
4.8k_CELP
- 语音CELP压缩解压源代码（C语音)- Pronunciation CELP compression decompression source code (C pronunciation) - * 4800 bps CELP Characteristics * * Spectrum Pitch Code Book * ------------- ---------------
SAMP
- 稀疏自适应匹配追踪算法，无需稀疏度，就可以重构原始信号。-Sparse adaptive matching pursuit algorithm, without sparsity, we can reconstruct the original signal.
CoSaMP
- CoSaMP算法，在重构信号时，选取两倍稀疏度的原子来恢复原始信号，再删去一倍稀疏度的原子。-CoSaMP algorithm, in the reconstructed signal, select twice the atomic sparsity to recover the original signal, and then deleting the double sparsity of atoms.
CSR_Denoising
- Weisheng Dong and Xin Li. "Sparsity-based Image Denoising via Dictionary Learning and Structural Clustering"基于聚类的图像稀疏去噪这篇论文的去噪代码。-Weisheng Dong and Xin Li. " Sparsity-based Image Denoising via Dictionary Learning and Structural Clustering" C
BCS-SPL-1.5-new
- Block-based random image sampling is coupled with a projectiondriven compressed-sensing recovery that encourages sparsity in the domain of directional transforms simultaneously with a smooth reconstructed image. Both contourlets as well as comp
PoissonRecon
- Poisson surface reconstruction creates watertight surfaces from oriented point sets. In this work we extend the technique to explicitly incorporate the points as interpolation constraints. The extension can be interpreted as a generalization of
4-chapter
- 信号稀疏度检测的matlab仿真程序。该方法能够检测出宽带信号的稀疏度，保证Compressive Sensing在宽带频谱检测中的准确运用。-Signal sparsity detection matlab simulation program. The method can detect the wideband signal sparsity ensure Compressive Sensing wideband spectrum sensing in the exact use.
inpainting
- For using this code need to use signal toolbox and general toolbox in your matlab Inpainting using sparse regularization. Consider the pepper image with many missing pixels. Assume that the image is noisy-free. (a) Inpaint the image by usi
(SAMP)-Sparsity
- 一个关于压缩感知的论文，使用迭代法求出最后的稀疏解-a method for compressed sensing
RICE-UNIVERSITY
- 标准压缩感知（CS）理论决定了可靠的信号恢复是可能给M= O（KLOG（N / K））的测量。我们证明了它可以通过利用超越简单的稀疏性和可压缩性由包括价值观和信号系数的位置之间的依赖关系更加逼真信号模型大大降低Mwithout牺牲的鲁棒性。-The standard compressive sensing (CS) theory dictates that robust signal recovery is possible from M=O(Klog(N/K))
compress_sensing_without_frame
- 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 se
SL0andOMP
- SL0算法是一种基于近似L0范数的压缩感知信号重建算法,它采用最速下降法和梯度投影原理,逐步逼近最优解,具有匹配度高、重建时间短、计算量低、不需要信号的稀疏度这个先验条件等优点。-SL0 algorithm is an approximate L0 norm based compressed sensing signal reconstruction algorithm, which uses the steepest descent method and gradient projection
8-(1)
- 图像修复是对图像中破损区域进行信息填充，以减少图像破损所带来的信息损失的过程。 传统的图像修复方法需要依赖图像的具体结构来制定相应的修复方法，压缩感知理论的提出，使得可以利 用信号的稀疏性来对图像进行修复。基于K 奇异值分解（KSVD）与形态学成分分析（MCA，Morphological Component Analysis）的图像修复方法首先采用形态学成分分析方法对破损图像进行特征分析，将其分解 为结构部分和纹理部分；然后基于学习型字典KSVD分别对这两部分进行过完备字典训练；