K-means代码

目的是改善影像质量，包括去除高频噪声与干扰，及影像边缘增强、线性增强以及去模糊等。分为低通滤波（平滑化）、高通滤波（锐化）和带通滤波。(The purpose is to improve the image quality, including the removal of high frequency noise and interference, and image edge enhancement, linear enhancement and deblurring. Into a low-pass filter (smoothing), high-pass filtering (sharpening) and bandpass filtering.)

基于马尔可夫随机场（MRF）的图像分割算法(Image segmentation algorithm based on Markov Random Field (MRF))

fbp算法实现CT重建，包括不同投影个数、探测器个数和噪声对重建后图像的质量的影响。(FBP CT reconstruction algorithm, including the number of different projection, and the number of detector noise on the reconstructed image quality.)

通过图像处理技术实现对眼睛中的细微血管的提取，并进行强度变换，使效果更好。(Through the realization of extraction of slight blood vessels in the eye image processing technology, and intensity transformation, so that the effect is better.)

说明：  JPEG2000 的一个实现，期中，wavelet.dll是提供的功能接口。(An implementation of JPEG2000, period, wavelet.dll is to provide a functional interface.)

这是一个比较全面的傅里叶变换，例子中含有测试的图片，能让人更加明白傅里叶变换的处理效果。(This is a more comprehensive Fourier transform, the example contains a picture of the test, can make people more aware of the Fourier transform of the treatment effect.)

运动目标检测与跟踪，包括目标定位，匹配与跟踪!(moving target detection and tracking, including Target, matching and tracking!)

说明：  本代码采用的是opencv，有视频采集和对采集的视频帧进行laplace处理的功能！(This code uses a opencv, with video capture and the capture of video frames laplace processing capabilities!)

图像边沿提取，程序简单，适合图像处理初学者。(Image edge extraction, the procedure is simple, suitable for image processing for beginners.)

图像修复是对图像中破损区域进行信息填充，以减少图像破损所带来的信息损失的过程。 传统的图像修复方法需要依赖图像的具体结构来制定相应的修复方法，压缩感知理论的提出，使得可以利 用信号的稀疏性来对图像进行修复。基于K 奇异值分解（KSVD）与形态学成分分析（MCA，Morphological Component Analysis）的图像修复方法首先采用形态学成分分析方法对破损图像进行特征分析，将其分解 为结构部分和纹理部分；然后基于学习型字典KSVD分别对这两部分进行过完备字典训练；最后利用训练得 到的字典实现对破损图像的修复。相比于传统的图像修复方法，该方法具有适应性强、修复效果好等优点(Image inpainting is to fill the missing data in corrupted images and thus to reduce the information loss of damaged image. Traditional inpainting algorithms are dependent on specific structure of target images compressive sensing theory makes is possible to realized image inpainting with signal sparsity. This paper proposes a novel inpainting algorithm based on KSVD and MCA algorithm, which first decomposes the image into texture part and structure part, and then trains the two dictionaries for these two parts with KSVD and reconstructs the original image with these two trained dictionaries. Experiment indicates that the proposed algorithm is of better adaptability and performance as compared with traditional algorithms.)