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4下载：
基于字典学习的稀疏编码，最新的版本，可以用于 window Linux和MacOs-Sparse Coding on Dictionary Learning，the new version. it can be applied to the windows, Linux and Mac
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C++做的简易电子词典。学习数据结构的可以看看。-C++ to do a simple electronic dictionary. Learning data structure can look.
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多种信号过完备字典学习算法的工具包，包含文献Surveying and comparing simultaneous sparse approximation (or group-lasso) algorithms中所有的算法。-Multiple signals over-complete dictionary learning algorithm toolkit, including literature Surveying and comparing simultaneous sparse
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CS理论里观测矩阵的优化算法以及训练稀疏字典的算法-optimization of measurement matrix and dictionary learning for sparse approximations
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VB开发的英汉小词典背单词软件，可用来当英语词典用，供英语学习-VB development of the English words back a small dictionary software, available to use when the English dictionary for English language learning
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一种基于图像训练的字典构造方法，可以用来进行压缩感知的基矩阵构造-Based on image training dictionary construction method can be used for compressed sensing-based matrix structure
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Metaface Learning for Sparse Fisher Discrimination Dictionary Learning for Sparse Representation
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用基于稀疏表示和KSVD字典学习去噪方法在对数域对SAR图像抑斑，本方法相对于一些经典的SAR图像抑斑方法，抑斑效果大大提高。-Based on the sparse representation and KSVD dictionary learning denoising method in the logarithmic domain，we despeckle SAR image, compared with some of the classic SAR image speckle sup
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在线字典的学习算法，此方法可以对大量的特征进行学习，完整的代码。-Online dictionary learning algorithm, this method can be for a lot of the characteristics of the study, the complete code.
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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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经典的ksvd字典学习算法,可用于信号去噪,图像重建等-Classical ksvd dictionary learning algorithm can be used to signal denoising, image reconstruction
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这个程序可以对KSVD进行综合测试。首先，会产生一个随机的正交字典，会产生一个ＫＳＶＤ算法输入，这个输入是一个数据信号集合，每个元素都是三维元素的线性组合。-In this file a synthetic test of the K-SVD algorithm is performed. First,
a random dictionary with normalized columns is being generated, and then
a set of data signal
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燕京大学硕士论文，关于稀疏编码，涉及到多层字典学习算法，很优秀的论文-Yenching University Master' s thesis on sparse coding, involving multi-dictionary learning algorithm, very good papers
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基于字典学习和块匹配的自然图像去噪方法，主要克服现有自然图像去噪中纹理细节易丢失和同质区域不平滑的问题。-Dictionary learning and natural image denoising method based on block matching, mainly to overcome the existing natural image denoising easily lost and homogeneous texture detail area is not smooth
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Convolutive dictionary learning
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Research Advances on Dictionary Learning Models, Algorithms and Applications
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使用基于稀疏表示和字典学习的图像去噪研究方法，比一般的帧间差分算法等要高效。-Sparse representation and dictionary-based learning denoising methods, than the average frame difference algorithm to be efficient.
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通过进行双字典学习来完成单幅影像超分辨率重建(use dual dictionary learning to single image super resolution)
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压缩感知，在线字典学习，稀疏编码，矩阵优化(Compression perception, online dictionary learning, sparse coding, matrix optimization)
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字典学习通过优化某个目标泛函, 由样本训 练出更适应于当前图像或者信号表示的自适应字典.(In dictionary learning, an adaptive dictionary which is more suitable for current image or signal representation is trained by optimizing a target function)
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