SSD%2BSAD%2BNCC算法

说明：  MATLAB实现的图像处理算法,计算图像中的欧拉数,(MATLAB realize the image processing algorithm to calculate the Euler number of images,)

几个将图像分块，将分块图像拼接，和裁减图像的实用小程序(several image block to block image mosaic, and the reduction of small images of practical procedures)

说明：  利用全卷积神经网络，实现图像的语义分割，基于tensorflow的keras可以直接运行(Using the full convolutional neural network to achieve semantic segmentation of images, keras based on tensorflow can be run directly)

本代码程序主要是关于轮廓波变换的程序，很有条理的描述了轮廓波的实现过程。(I hoe this code can offer the help.)

（1） 进行小波的图像消噪处理 （2） 进行小波的图像压缩处理 （3） 进行小波的图像增强处理 ((1) Wavelet Image Denoising (2) Wavelet Image Compression (3) Wavelet Image Enhancement)

品质因子可调的离散小波变换工具箱，可通过参数灵活控制小波函数的频域窗宽(Discrete wavelet transform toolbox with quality factor adjustable, flexible control parameters through the wavelet function in the frequency domain window width)

说明：  n个点电荷电场线的matlab仿真 分别有1个 2个等（可以通过改变程序数字来得到相应的仿真结果）(Matlab simulation of N-point charge electric field lines)

(cvpr)Matching Local Self-Similarities descriptor自相似描述子((cvpr)Matching Local Self-Similarities across Images and Videos)

对两幅图像进行配准，分别提取两幅图像的surf特征点以及描述子，得到粗匹配结果，然后根据粗匹配结果，采用ransac方法计算基础矩阵，并去除误匹配点，得到较准确匹配结果(Two image registration, surf was extracted from the feature points in two images to get the coarse matching and descriptor, then according to the results, the coarse matching results, using RANSAC method to calculate the fundamental matrix, and eliminate the error matching, gain a more accurate matching results)

针对可见光与红外图像的特点和难点，提出了可见光与红外图像配准与融合中的关键技术，即: 使用新型的基于一维最大类间方差和最大连通性测量的图像分割方法对源图像进行分割来更好地实行图像粗 配准 使用新型的特征点提取方法，特征点的匹配及误匹配的消除来更好地实行图像精配准 采用新型的基 于区域的树状小波活性测度计算来实现树状小波图像融合 利用自生成神经网络来实现模栩图像融合. (For visible light and infrared images of the characteristics and difficulties, a visual and infrared image registration and integration of key technologies, namely: the use of new one-dimensional based on the largest variance between-class connectivity and the largest measuring method of image segmentation source image segmentation to better implementation of coarse image registration the use of a new type of feature point extraction method, feature points of the match and the elimination of false matches to better the implementation of image fine registration the use of new tree-based wavelet calculated to measure the achievement of the activity tree wavelet image fusion the use of self-generating neural network to achieve image fusion Xu mode.)