SWT

自己用MATLAB编的一维信号数学形态学的腐蚀与膨胀运算以及开闭运算，可用于滤去信号中的尖锐脉冲信号，适合于用于振动信号的预处理(Own series of one-dimensional signal using MATLAB mathematical morphology, corrosion and expansion of operations, as well as the opening and closing operation can be used to filter the signal sharp pulse signal suitable for vibration signal preprocessing)

针对彩色图像的局部直方图均衡化，可以较好的增强图像细节(poshe,it can enhancements the detail of the image )

使用sift算法，找出图像的关键点，使用关键点作为匹配因子对两幅图像进行匹配，数据库中包含了丰富的可用图片库。(Use sift algorithm to find the image of the key points, key points to use as matching factor of two images match, the database contains a wealth of available gallery.)

边缘检测与提取，轮廓跟踪 注意事项： 运行时，文件c: est.bmp必须存在(Edge detection and extraction, contour tracing Note: run-time, file c: test.bmp must exist)

可以计算mtf，使用matlab输入图像即可实现计算(You can calculate MTF and use matlab to input images.)

高光谱遥感影像数据降维处理 包括线性降维和非线性降维处理(Hyperspectral remote sensing image data to reduce the dimension of linear dimensionality reduction and nonlinear dimensionality reduction processing)

图像处理中的数字识别代码，包括数字训练集合和训练分类算法，可以实际应用。(digital recognition)

基于多分辨率的NSST变换工具箱，用于图像融合效果好，且速度优于NSCT。(Based on Multi-resolution NSST conversion kit for image fusion effect is good, and the speed is better than NSCT.)

VC图像编辑软件 就要卷积模糊和卷积模糊加噪 以不同方法（大于等于2）实现图像的模糊效果（局部或全局）。并以BMP格式存储。(soft)

本算法在Matlab2008b 环境下实现。包括main,san 和ssd 三个函数。 这次实现的算法并不是比较两个已经知道的点是否匹配，而是已知一个图形中的特征点，在另外一个图像中找到与其最匹配的点，匹配度用SAD 或者SSD 来度量。 main.m 是程序的入口，包括生成和读入实验数据，调用sad 函数和ssd 函数求匹配点，最后绘 制出最后的结果。sad.m 是用sad 度量方法在另一图中求解匹配点的函数实现，有3 个参数 y=sad(x,image1,image2)。其中x 是代匹配的数据，image1 是图像1 数据，image2 是图像2 数据。 从理论上分析，ssd 比sad 算法要复杂一点，经过测试，在一幅640*480 的图像中寻找10 个匹配点数据SAD 用时25.062519 秒，SSD 用时25.291432 秒。(The algorithm Matlab2008b environment to achieve. Including the main, san, and ssd three functions. The implementation of the algorithm is not the point of comparing two matches already know, but the known feature points in a graphic, an image found in the other match with the most points, matching measured with the SAD or SSD. main.m is the entry, including the generation and reading test data, call the sad ssd function evaluation functions and matching points, and finally draw the final result. sad.m measure is sad figure in another match point in the function implementation to solve, there are three parameters y = sad (x, image1, image2). Where x is the generation of matching data, image1 is the data image 1, image2 is the image 2 data. From the theoretical analysis, ssd little more complicated than the sad algorithm, tested in a 640* 480 images of 10 match points in the search for data using time 25.062519 seconds SAD, SSD with time 25.291432 seconds. )