main

论文+ppt，我的数字图像处理课程设计，题目是基于均值滤波和小波分析的图像去噪方法(Thesis+ppt, digital image processing course design, subject image denoising method based on mean filter and wavelet analysis)

这是一个比较完整的KPCA算法，通过这个算法可以快速的找到主要的特征向量进行主成分分析。(This is a relatively complete KPCA algorithm, this algorithm can quickly find the main feature vector principal component analysis.)

包含二维广义S变换程序和测试程序，用于对图像进行时频分析。matlab版本R2011b,操作系统为32位win7。在2G内存下最大只能对84*84的图像进行处理，更大的图像会出现out of memory的问题。目前网上还没有这个程序，包括本站，pudn，ilovematlab都没有。把程序上传到这三个地方，望大家一起来改进它。(Contains a two-dimensional generalized S transform procedure and testing procedure.It is used for the time-frequency analysis of images.Matlab version :R2011b, operating system: 32 win7. Tests under 2G memory can only process 84* 84 images.A larger image will incur "out of memory" problems. This program is not found online before, including this site, CSDN, ilovematlab. Upload the program to the above three websites, hoping it improved by the help of everyone.)

图像视差获取，效果还不错,可用于三维重建、等领域。(Parallax image acquisition, the results were good, can be used for three-dimensional reconstruction, and other fields.)

说明：  这是我编写的关于计算机视觉中的一种重要的变换---射影变换的matlab源代码，希望对您有用。(This is what I prepared on computer vision in an important transformation of the projective transformation--- matlab source code, I hope useful for you.)

fcm医学图像分割算法，适应于不规则医学图像的分割，已经调试成功，可以运行，很好。相信会对您有帮助。(fcm medical image segmentation algorithm, adapted to irregular medical image segmentation, has been successful debugging, you can run, very good. Believe will help you.)

数字图像处理，C语言，功能是打开一幅位图，实现几何变换。(Digital image processing, C language function is to open a bitmap, to achieve geometric transform.)

用小波域隐马尔科夫树模型进行图像降噪的matlab软件，rice大学编制(Software For Image Denoising Using Wavelet-Domain Hidden Markov Tree Models)

说明：  基于Halcon开发的掩模 纯手工编辑(Mask editing based on Halcon)

基于多帧图像插值（Interpolation）技术的方法是SR恢复技术当中最直观 的方法。这类方法首先估计各帧图像之间的相对运动信息，获得HR图像在非均 匀间距采样点上的象素值，接着通过非均匀插值得到HR栅格上的象素值，最后 采用图像恢复技术来去除模糊和降低噪声（运动估计!非均匀插值!去模糊和 噪声）。(In this paper, we propose a novel method for solv- ing single-image super-resolution problems. Given a low-resolution image as input, we recover its high- resolution counterpart using a set of training exam- ples. While this formulation resembles other learning- based methods for super-resolution, our method has been inspired by recent manifold learning methods, par- ticularly locally linear embedding (LLE). Speci?cally, small image patches in the low- and high-resolution images form manifolds with similar local geometry in two distinct feature spaces. As in LLE, local geometry is characterized by how a feature vector correspond- ing to a patch can be reconstructed by its neighbors in the feature space. Besides using the training image pairs to estimate the high-resolution embedding, we also enforce local compatibility and smoothness con- straints between patches in the target high-resolution image through overlapping. Experiments show that our method is very ?exible )