搜索资源列表
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romp算法实现基于稀疏表示的图像超分辨率算法字典对的训练，本代码为matlab文件 -Romp algorithm to realize image super-resolution algorithm based on sparse representation of a dictionary of training, the code for the matlab files
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matlab code of A hybrid approach combining extreme learning machine and sparse representation for image classification. Engineering Applications of Artificial Intelligence 27 (2014): 228-235.-Luo, Minxia, and Kai Zhang. A hybrid approach combining e
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稀疏表示的matlab代码，可以对信号进行稀疏表示，对稀疏表示有兴趣的学者，可以研究，很有帮助-Sparse representation of the matlab code, and the signal can be sparse representation, interested in sparse representation of the scholar, to study, very helpful
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本代码主要实现了基于双字典交叉稀疏表示的SAR图像变化检测的功能。-This code mainly realize the function of SAR image change detection is based on the double cross dictionary sparse representation.
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基于低秩和稀疏表达的迁移子空间学习算法的代码(A migration subspace learning algorithm based on low rank and sparse representation)
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图像去噪修复的典型算法代码包，包含基于稀疏表示的KSVD图像去噪，BM3D彩色图像去噪代码，TV图像修复代码，MCA图像修复代码。(Typical image denoising algorithm code package, including sparse representation based on KSVD image denoising, BM3D color image denoising code, TV image restoration code, MCA image res
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稀疏与冗余表示-理论及其在信号与图像处理中的应用一书的源代码(Sparse and redundant representation -From theory to application in signal and image processing -- the source code of the Book)
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该代码是用于稀疏表示的工具包，里面的content有具体说明。(This code is a toolkit for sparse representation, where content is specified.)
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本资源是3层的自编码器加上稀疏正则项约束的matlab代码。隐层激活函数选sigmoid函数，输出层选线性函数，程序中以一个标准数据集sonar为例，使用该方法可以做无监督表征学习，数据压缩，多任务学习等(This resource is a 3-layer self-encoder plus matlab code for sparse regular term constraints. The hidden layer activation function selects the sigm
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稀疏表示分类器应用于高光谱图像分类的MATLAB代码实现(MATLAB Code Implementation of Sparse Representation Classifier for Hyperspectral Image Classification)
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This package contains the code which is associated with the following papers:
Yu Liu, Zengfu Wang, "Simultaneous image fusion and denoising with adaptive sparse representation". IET Image Processing,vol.9, no.5 ,pp.347-357, 2015
Edite
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