搜索资源列表
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该工具箱为KSVD算法的实现，其用来自适应地对信号就行稀疏表示，达到良好的稀疏性-The toolbox is KSVD algorithm, it is used to adaptively sparse representation of the signal on the line to achieve a good sparse
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附件程序为图像稀疏表示的源代码，程序用matlab编写，能正常运行。-Sparse representation of the image attachment program source code, programs written with matlab able to function properly.
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KSVD原始算法：信号稀疏表示中的过完备字典的学习算法-KSVD original algorithm: Signal Sparse Representation of the learning algorithm over-complete dictionary
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稀疏表示正交匹配追踪算法matlab源代码-sparse representation
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稀疏编码的工具包，用matlab实现，数学上是求解l1 norm最小化-toolkit for sparse coding
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Images Denoising via Sparse and Redundant Representation
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带有visual接口的matlab稀疏表示算法，以及对应的文章-Visual interface with matlab sparse representation algorithm, and the corresponding articles
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自己写的Gabor方程函数，可以用来产生过完备的稀疏表示字典。-Gabor write their own equation function, can be used to generate the sparse representation over-complete dictionary.
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Image Super Resolution via Sparse Representation. -Image Super Resolution via Sparse Representation.Image Super via Sparse Representation.
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图像稀疏表示的MATLAB编程代码，有图-MATLAB sparse representation of the image programming code, a map
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參考文獻「Sparsity-based Image Denoising via Dictionary Learning and Structural Clustering」
Clustering-based Sparse Representation 的 matlab代碼-「Sparsity-based Image Denoising via Dictionary Learning and Structural Clustering」and
Clustering-based Sparse
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參考文獻「Gabor Feature based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary」and matlab code.-「Gabor Feature based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary」and matlab code.
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本matlab源程序适用于论文 metasample-based sparse representation for tumor classification
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极限学习机\极速学习机\ELM
稀疏表示人脸识别\稀疏表示\L0范数求解
基于ELM与稀疏表示的混合人脸识别算法
AR人脸识别准确率95 .
文章:Luo, Minxia, and Kai Zhang. A hybrid approach combining extreme learning machine and sparse representation for image classification. Engineering Applications of Artific
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一种在matlab环境下的计算稀疏表示的学习字典的算法(A learning dictionary algorithm for computing sparse representation in Matlab environment)
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MATLAB implementation of compressive sensing example as described in R.
Baraniuk, Compressive Sensing, IEEE Signal Processing Magazine, [118],
July 2007. The code acquires 250 averaged random measurements of a 2500
pixel image. We assume that the ima
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MATLAB implementation of compressive sensing example as described in R.
Baraniuk, Compressive Sensing, IEEE Signal Processing Magazine, [118],
July 2007. The code acquires 250 averaged random measurements of a 2500
pixel image. We assume that the
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Image super-resolution via sparse representation
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提出了一种新的结构稀疏跟踪算法，它不仅利用候选目标及其局部块之间的内在关系，学习它们的联合稀疏表示，而且保留了各候选目标内的局部块中的空间布局结构。(A new structure sparse tracking algorithm is proposed. It not only uses the inherent relationship between candidate targets and their local blocks to learn their joint sparse
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组稀疏表达Matlab代码，同时里面包含了压缩感知信号稀疏表征的几种典型算法。(G r o u p S p a r s e B o x, the matlab codes for group sparse representation, some classical algorithms such as BMP, BOMP, StGOMP etc. also included.)
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