搜索资源列表
NLMeans
- 基于NL_Means的均值平移图像分割算法-NL_Means based on the mean shift image segmentation algorithm
toolbox_nlmeans
- non local mean algorithm toolboc-non local mean algorithm toolboc
NLMeans
- 非局部均值滤波的算法，还不错哦~自己觉得很好-Non-local mean filter algorithm, is also a good feel oh well
nlmeansC.tar
- nlmeans C编写的，希望对大家有用-nlmeans written in C, and the hope that useful
NLmeansfilter_pca
- 非局部图像去噪算法的实现,使用pca提高运算速度-the implementation of nlmeans using pca to reduce computation
NLmeans-and-lpfilter
- 数字图像处理中的图像去噪部分，用低通去噪和non-local means两种方法，其中用non-local means，可把信噪比提高10dB左右。-Digital image processing, image denoising part of the low-pass de-noising and non-local means are two ways in which non-local means to signal to
NLMeans
- 非局部均值滤波 输入: 待平滑的图像 t: 搜索窗口半径 f: 相似性窗口半径 h: 平滑参数 NLmeans(ima,5,2,sigma)-Non-local mean filtering input: to be a smooth image t: the search window radius f: similarity of the window radius h: smoothing p
NLmeans
- 消除图像噪声，凸显图像结构。非局部滤波应用于图像去噪。-Implementation of the Non local filter proposed for A. Buades, B. Coll and J.M. Morel in "A non-local algorithm for image denoising"
NLMeans
- 非局部均值去除图像噪声，matlab程序实现，非常有参考价值-Non-local means to remove image noise, matlab program, very useful
NLMeans
- 非局部均值图像去噪，基于搜索窗 区域相似性-Non-local means image denoising based similarity search window area
nlmeans-patch
- 在matlab环境下利用非局部均值滤波对图像处理的SURE估计，很好的程序。-In the matlab environment using nonlocal average filtering SURE estimation for image processing, very good program.
nlmeans_version2
- 非常有名的NLmeans算法的实现，包含C++和m文件，值得研究-NLmeans algorithm to achieve very famous, including C++ and m files, worthy of study
My-NLMeans
- 采用非局部均值滤波（NLM filter）对图像进行去噪，能取得很好的效果。-Using non-local means filter (NLM filter) for image de-noising, can achieve good results.
toolbox_nlmeans1
- nlmeans matlab toolbox
_nlmeans
- nlmeans matlab toolbox include necessary functions
__MACOSX
- nl means toolbox for mac systems
fastNLmeans
- 快速NLmeans函数,基于积分图像的改进，让算法效率提高十倍左右。(The fast NLmeans function, based on the improvement of the integral image, makes the efficiency of the algorithm up to about ten times.)