拉普拉斯锐化（边缘锐化）：对图像边缘进行拉普拉斯锐化

利用形态学方法寻找图像中互相重叠的点 对其进行分类并最后消除。-using morphological methods for image overlapping points classification and its final elimination.

UltraFractal出品，长于自定义公式，创建新的分形，色彩设置很有特色，特别是zoom的时候，非常平滑，非常有特色。另外软件的外观也很舒服。目前最新的版本是3.05。 -UltraFractal Chupin, longer than the custom formulas, create a new sub-shape, the color set is very unique, particularly when the zoom is very smooth, very unique. In addition the appearance of the software is also very comfortable. The most recent version is 3.05.

国外著名大学成功的人脸检测示例中一部分！包含多种环境下的代码-famous University of face detection illustration part! Covering a wide variety of environment code

给予偏微分方程的图像处理一书中的实验代码很全面 -Given partial differential equations of image processing of the experimental code book is very comprehensive

一系列展示图像压缩技术的源代码。包括有：使用块截断编码的图像压缩(Block Truncation)、基于高斯金字塔变换的图像压缩(Gaussian Pyramids)、基于离散余弦变换对图像压缩(Discrete Cosine Transform)、基于奇异值分解(SVD)的图像压缩(Singular Value Decomposition)。给出的代码还可以用于2D图像噪声消除-A series of image compression technology to display the source code. Include: the use of block truncation coding for image compression (Block Truncation), based on the Gaussian pyramid transform for image compression (Gaussian Pyramids), based on the discrete cosine transform for image compression (Discrete Cosine Transform), based on singular value decomposition (SVD) Image compression (Singular Value Decomposition). Given code can also be used in 2D image noise cancellation

基于混合高斯的背景建模与阴影抑制算法研究,背景建模，目标检测-Based on Gaussian mixture background modeling and shadow suppression algorithm, background modeling, target detection

图像卡的PCB图，想做图像卡的可以看看，绝对好！！用PADS2005打开-image of the PCB card plans, do images can look at the card, absolutely! ! Opened with PADS2005

此为经过调试过的边缘检测的源代码，用其能够实现精确的边缘检测.-This is the result of edge detection have been debug the source code, can be achieved with its precise edge detection.

Super Resolution of Images and Video 超分辨率入门书籍-Super Resolution of Images and Video

图像边缘检测及颜色发现，如果你正在学习边缘检测的阈值，那么这些程序对你是有用的。-Color Image Edge Detection and found that if you are learning to edge detection threshold, then these procedures are useful to you.