搜索资源列表
Bayesian-Image-code-new
- 使用改进型贝叶斯对矩阵进行低秩处理，进而应用到图像处理方便-Improved Bayesian use low rank matrix processing, and then applied to the image processing convenience
LowPatchRank_regularization
- 对于带噪声的周期性纹理图像,提出一种基于二维秩约束的混合正则化去噪方法。该方法结合了全变分去噪理论和方法，并且利用该类图像低块秩的特性，对图像进行了低块秩约束。通过和全变分去噪方法比较可知，对于周期性纹理图像，混合正则化方法能有效地分离出噪声，并且能让图像很好地保持边缘。即使非严格的周期性纹理，该方法依然有很好的去噪效果。-For periodic texture images with noise, a new method based on two dimensional rank cons
DIP
- 包含基于三种不同方法的应用于图像处理的矩阵低秩分解的matlab算法，可以将目标图像分解为一个低秩图像和一个稀疏图像之和；并包含一种基于区域生长方法的图像区域识别程序，可以用来提取图像中的目标。-Containing used in image processing based on three different methods of low-rank matrix decomposition algorithm matlab, the target image can be decompos
aprgca
- 基于近邻梯度法的低秩矩阵重构程序，可以用于图像分割，将目标图像分割为背景和前景，从而将前景分离出来。-Based neighbor gradient method low rank matrix reconstruction procedures can be used for image segmentation, the target image into background and foreground, which will separate the foreground.
IALM
- 基于不精确拉格郞日算子法的低秩矩阵重构程序，可以用于图像分割，将目标图像分割为背景和前景，从而将前景分离出来。-Lagrangian method based on imprecise low rank matrix reconstruction procedures can be used for image segmentation, the target image into background and foreground, which will separate the foregr
lrsdrpca
- 基于LRSD方法的低秩图像分解程序，参数可以在运行中依据目标图像的实际情况进行调节，注意目标图像不要过大，否则会溢出。-Image decomposition process based on low rank LRSD method parameters can be based on the actual situation in the operation of the target image is adjusted, note the target image is not too l
svtrpca
- 基于特征值分解的低秩图像分解程序，将目标图像分割为背景和前景之和，参数可以在运行中依据目标图像的实际情况进行调节，注意目标图像不要过大，否则会溢出。-Based on Eigenvalue Decomposition of low rank image decomposition process, the target image into the background and the foreground and, in the operation parameters can be base
Nonlocal-Image-Restoration-
- 利用非局部特性并结合低秩解法的图像降噪算法，速度很快-Nonlocal properties combined with low rank Solution image noise reduction algorithm, fast
sparse_graph_LRR
- 关于文献“ Laplacian Regularized Low-Rank Representation and Its Applications”的matlab 源码-with regard to the paper Laplacian Regularized Low-Rank Representation and Its Applications matlab code
lrr(motion_face)
- 本程序是用来处理图像，把代表图像的矩阵分解成为一个低秩矩阵和一个稀疏矩阵~-This procedure is used to deal with the image, the representative image of the matrix decomposed into a low-rank matrix and a sparse matrix ~
sparse-denoise
- 使用图像的低秩约束去除图像中稀疏噪声，同时施加了噪声的稀疏约束-The use of images of low rank constraint removed sparse image noise while applying noise sparse constraint
RMSC
- Robust Multi-View Spectral Clustering via Low-Rank and Sparse Decomposition
SVT
- SVT 低秩矩阵恢复，可用于稀疏表示、图像恢复(SVT for low rank matrix completion)
稀疏分解图像去噪
- 基于稀疏字典和稀疏编码的图像去噪算法，基于低秩约束的高光谱条纹噪声去除，包含论文及代码(Based on sparse dictionary and sparse coding image denoising algorithm, based on low rank constraints of hyperspectral fringe noise removal, including papers and code)
lrslibrary-master
- 前景提取算法集合，用matlab做的前端(The LRSLibrary provides a collection of low-rank and sparse decomposition algorithms in MATLAB. The library was designed for motion segmentation in videos, but it can be also used or adapted for other computer vision problems (fo
APGL
- 用于求解凸优化问题，恢复低秩密度矩阵问题(this matlab Algorithm can be used for the convey optimal problen, and also for the low-rank density matrix reconstruction)
SRF
- 一种低秩填充算法，SRF算法。通过低秩及相关性恢复矩阵数据。(Low rank filling algorithm)
matrixcompletion
- 矩阵完成算法 具有辅助信息的低秩矩阵完成模型(A low-rank matrix completion model with side information)
PCLR
- 利用图像的非Denoising of noisy image using non local joint low rank feature of image局部联合低秩特性，实现含噪图像去噪(Denoising of noisy image using non local joint low rank feature of image)
pcadenoise
- 矩阵 pca或者低秩方法去噪，利用svd分解，实现对图像矩阵的去噪，该方法支持对rgb图像的去噪。使用代码请 文章中表明出处，感谢。 感谢重庆市研究生科研创新项目支持，项目号CYS16183(image denoise by low-rand regularizer or pca method. the low rank is evaluted by svd, and this method is also support for rgb image.)