合成孔径雷达点目标仿真 SAR CS

纹理图像分割的算法和实例，其中包括理论的讲解和 MATLAB 文件。(Algorithms and Examples of Texture Image Segmentation)

求信噪比的MATLAB源代码 很实用的函数(Signal to noise ratio for the MATLAB source code is very useful function)

利用MATLAB实现图像处理，实现图像背景分离。(Using MATLAB to realize image processing)

生成一个Gabor Patch的图形，可以调整图形尺寸、相位、旋转角度、对比度等各种参数，在基于PsychToolbox的实验程序编程中非常实用，可用作视觉刺激图形(Generate a Gabor patch, parameters like size, phase, angle and contrast are modifiable, useful in PsychToolbox programming, can be used as visual stimuli)

FIR滤波器的C语言实现 C语言源程序代码及解析(FIR filter, the C language realization C source code and analytical)

基于matlab编写的数字图像增强，包括直方图均衡化，高斯滤波。。。。(Digital image enhancement based on matlab, including histogram equalization, gaussian filtering...)

说明：  使用gpu、cpu并行进行sift算子计算匹配，能够在原来的基础上加速处理，但对显卡要求较高，具体环境配置使用方法可以参照mannual(SiftGPU is an implementation of SIFT [1] for GPU. SiftGPU processes pixels parallely to build Gaussian pyramids and detect DoG Keypoints. Based on GPU list generation[3], SiftGPU then uses a GPU/CPU mixed method to efficiently build compact keypoint lists. Finally keypoints are processed parallely to get their orientations and descriptors. SiftGPU is inspired by Andrea Vedaldi s sift++[2] and Sudipta N Sinha et al s GPU-SIFT[4] . Many parameters of sift++ ( for example, number of octaves, number of DOG levels, edge threshold, etc) are also available in SiftGPU. The shader programs are dynamically generated according to the parameters that user specified. SiftGPU also includes a GPU exhaustive/guided sift matcher SiftMatchGPU. It basically multiplies the descriptor matrix on GPU and find closest feature matches on GPU. Both GLSL and CUDA implementations are provided. )

说明：  计算图像峰值信号比，比较边缘检测图像的质量(Calculate the image peak signal ratio and compare the quality of edge detection)

改变图像灰度，增强图像中需要的信息，可利用与分割前处理(Change the image gray level, increase the information)

针对现有方法存在的隐藏容量低、操作不灵活的缺陷，文章提出了一种基于无损压缩的加密图像可逆 信息隐藏算法。(In view of the defects of low hiding capacity and inflexible operation of the existing methods, this paper proposes a reversible information hiding algorithm for encrypted images based on lossless compression.)