KFCM

说明：  基于图像边缘特征的图像放大增强算法，提高图像的分辨率。(The edge of the characteristics of image-based image enhancement algorithms to enlarge and enhance image resolution)

该代码为用tamura算法提取图像的纹理特征，包括粗糙度、对比度等，可用于基于内容的图像检索系统(The code for the algorithm used tamura extracted texture features, including roughness, contrast, etc., can be used for content-based image retrieval system)

说明：  c语言处理运动图像 适合初学者 对一些基本的函数介绍的很详细(c language processing motion picture)

遥感影像处理，可以用于盲源信号分离，快速高效的独立成分分析方法(Remote sensing image processing, can be used for blind source separation, rapid and efficient method of independent component analysis)

一段修改后的变色龙聚类算法，可用于无监督聚类。(A modified chameleon clustering code, using matlab.)

图像的三种分水岭分割方法，一般分水岭分割，使用梯度的两次分水岭分割及使用梯度加掩模的三次分水岭算法(images of the three watershed segmentation method, the general watershed segmentation, gradient of the two watershed segmentation and the use of the gradient increases mask the three watershed algorithm)

image processing for hand guesture

序列图像中运动小目标检测跟踪，实用性很强，张天序教授的硕士生撰写的学位论文(Image sequences small moving target detection and tracking, very practical, Professor Zhang Tianxu of graduate students writing dissertations)

实现了单色图像的JPEG2000编码图像和解码图像！计算其压缩比和误差，观察源图像和重构图像之间的差别！(JPEG2000 images to achieve a monochrome image coding and decoding image! Calculate the compression ratio and error, observation of the source image and the difference between the reconstructed image!)

实现简单图像处理，包括256色转灰度图、Hough变换、Walsh变换、中值滤波、二值化变换、亮度增减、傅立叶变换、反色、取对数、取指数、图像平移、图像旋转、图像细化、图像缩放、图像镜像、均值滤波、对比度拉伸、拉普拉斯锐化（边缘检测）、方块编码、梯度锐化、灰度均衡、直方图均衡、离散余弦变换、维纳滤波处理、逆滤波处理、阈值变换、高斯平滑。(simple image processing, including color to grayscale 256, the Hough transform, Walsh transform, Median filtering, binary conversion, brightness changes, Fourier transform, and color-from the few to admit index, image shifting, image rotation, refined images, image scaling, image mirroring, mean filtering, contrast stretching, Laplacian sharpening (edge detection), block codes, Sharpening gradient, gray balance, histogram equalization, discrete cosine transform, Wiener filter, reverse filtering, threshold transform, Gaussian smoothing.)