搜索资源列表
Compression-sensing
- 压缩传感理论，用K-SVD算法训练字典，过完备字典-Compression sensing theory, SVD algorithm with K- had complete training dictionary, dictionary
k_svd
- k-svd算法m代码.用于形成冗余字典，对图像进行稀疏分解-m k-svd algorithm code used to create a redundant dictionaries, sparse decomposition of images
KSVD_Matlab_ToolBox
- The K-SVD is a new algorithm for training dictionaries for linear representation of signals. Given a set of signals, the K-SVD tries to extract the best dictionary that can sparsely represent those signals.
ImprovedDL
- 这是一篇SCI文章《改善字典学习：多字典更新和系数重用》里的Matlab代码，包含了OMP、Batch-OMP、CoROMP等匹配跟踪算法代码以及改进的K-SVD字典学习算法代码，是图像稀疏表示研究方向重要的源代码，有助于大家学习和改进。-This file folder reproduces the Figures for paper:"Improving Dictionary Learning: Multiple Dictionary Updates and Coefficient Reus
KSVD_Matlab_ToolBox
- K-SVD算法应用，图像去噪，提高图像质量-The K-SVD is a new algorithm for training dictionaries for linear representation of signals. Given a set of signals, the K-SVD tries to extract the best dictionary that can sparsely represent those signals.
OMP
- 经典的OMP算法，应用于构建字典，稀疏系数等，可搭配K-svd中-Classic OMP algorithm, which used to build the dictionary, sparse coefficient, etc., can be used with the K-svd
K-SVD
- K-SVD是一种经典的字典训练算法，依据误差最小原则，对误差项进行SVD分解，选择使误差最小的分解项作为更新的字典原子和对应的原子系数，经过不断的迭代从而得到优化的解。-K-SVD is a classic dictionary training algorithm, based on the principle of minimum error, the error items SVD decomposition, choose to make the smallest error decom
Dictionray-Learning
- 压缩感知理论中字典学习算法的实现和对比。2015年新提出的解析解算法和传统的K-SVD算法进行了对比试验。-Compressive sensing theory and comparative dictionary achieve learning algorithms. Analytic solution algorithm and the traditional K-SVD algorithm proposed in 2015 the new test were compared.
K-SVD-algorithm
- KSVD算法，对于字典训练很实用，并且，对于基于稀疏表示方法，可以提高精度-KSVD algorithm for dictionary training is very practical，And, based on sparse representation method, can improve the precision
Ativnoisinmeans-c
- An adaptive image denoising technique based on the nonlocal means (NL-means) algorithm is investigated in this research. The proposed method first employs the singular value decomposition (SVD) method and the K-means clustering (Kmeans) techn
denoiseImageKSVD
- 图像处理中稀疏表示中的ＫＳＶＤ算法，用于求解稀疏系数-Image processing sparse representation K SVD algorithm for solving sparse coefficient
CSR_Denoising
- 该算法首先通过字典学习得到含噪图像的冗余字典，然后对相似的图像块进行聚类构成块群，并通过迭代收缩和L1正则化约束，对同类的图像块在字典上进行稀疏表示，以达到降噪的目的。实验结果表明，在常规的图像处理上，本文提出的算法能较好的保留图像的结构信息，与K-SVD和BM3D等现有的流行算法相比，具有更高的峰值信噪比（PSNR）-It firstly get the redundant dictionary of a noised image by dictionary learning.Then,the
K-SVD_SOMP-master
- 基于K-SVD方法的图像去噪算法，代码是MATLAB版的(Image denoising algorithm based on K-SVD method, the code is MATLAB version of)
K-SVD
- KSVD的稀疏编码去噪的matlab版本算法(Sparse encoding KSVD denoising algorithm)