histeqgray

说明：  SAR影像单视数据转为多视数据，数据间相互转化，快捷处理SAR数据(Single-look SAR image data into a more visual data, data inter-conversion, fast processing SAR data)

基于规格化Hough变换的天波超视距雷达检测前跟踪算法(Hough transform based on the normalized horizon radar detection and tracking algorithms)

基于侧抑制的图像分割，在ＰＣＮＮ分割的基础上实现(failed to translate)

Tamura纹理特征提取及应用的matlab代码，附有介绍绍Tamura纹理的文献和测试用图片。，已通过测试。 (Tamura texture feature extraction and application of the Matlab code, accompanied by Shao Tamura texture literature and test pictures. , Has been tested.)

自己编写的数字图像处理程序，包括打开bmp图像，直方图，阈值分割，形态学处理等功能。(digital image processing procedures, including open bmp image, histogram, threshold segmentation, morphological processing and other functions.)

图形图像正交变换，离散余弦变换，VC++实现。(Graphic image orthogonal transform, discrete cosine transform, VC++ to achieve.)

锐化图片 拉普拉斯算法 两种方法 一种是自己写的一种是用了matlab里面自带的函数(ruihua picture Laplace there are two mechof a fi [as fs aflasl;nvb)

四叉树分割 opencv 编写 读入灰度图 进行递归分割(Quadtree segmentation opencv read into grayscale prepared for recursive partitioning)

多基线干涉合成孔径雷达干涉条纹的获取，通过两幅单视复图像进行配准，获取干涉相位(Multi-baseline interferometric synthetic aperture radar interferometry fringes of the acquisition, through two single look complex image registration, access to intervention phase)

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. )