kkmatlabrenlian

基于小波的去噪算法，采用BAYES原理选取阈值，源代码，参考文献：Adaptive Wavelet Thresholding for Image Denoising and Compression(Wavelet-based denoising algorithm using the principle to select threshold BAYES, source code references: Adaptive Wavelet Thresholding for Image Denoisingand Compression)

说明：  主成分分析算法提取特征值，使用KNN分类器，K值可调(principal component analysis)

粒子滤波 图像处理程序,内有5个用于图像处理的程序(partical filter)

图像去雾基于暗原色先验去雾，何凯明算法的源代码改进，(Dark colors on the image to fog prior to fog, He Kaiming algorithm source code improvements,)

说明：  用matlab实现了FCM图像分割，分割效果较好。有说明(Matlab achieved using FCM image segmentation, segmentation better. Described)

基于仿射变换的图像匹配程序，这是基于mablab的一个主程序(Affine transformation based on the image matching process, which is based on a main mablab)

压缩感知的OMP算法应用于图像压缩与恢复。(OMP compressed sensing image compression and restoration algorithms applied to)

高阶频谱分析工具箱，老外写的代码，可以用来做实验(High-order spectral analysis toolbox, a foreigner writing code that can be used to experiment)

说明：  Z源逆变器开路三相SVPWM调制, 注入了三次谐波(Open-circuit Three-phase SVPWM Modulation of Z-source Inverter)

cvpr2012_oral On Multiple Foreground Cosegmentation(In this paper, we address a challenging image segmentation problem called multiple foreground cosegmentation (MFC), which concerns a realistic scenario in general Webuser photo sets where a finite number of K foregrounds of interest repeatedly occur over the entire photo set, but only an unknown subset of them is presented in each image. This contrasts the classical cosegmentation problem dealt with by most existing algorithms, which assume a much simpler but less realistic setting where the same set of foregrounds recurs in every image. We propose a novel optimization method for MFC, which makes no assumption on foreground configurations and does not suffer from the aforementioned limitation, while still leverages all the benefits of having co-occurring or (partially) recurring contents across images. Our method builds on an iterative scheme that alternates between a foreground modeling module and a region assignment module, both highly efficient and scalable. In part)