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一个基于DFT的水印算法源码；其中有嵌入，提取，测试图片；还有攻击程序，可以用来测试(DFT-based Watermarking Algorithm source including embedding, extraction, testing picture there are attacks on the procedures that can be used to test)

通过GAN和WGAN在python的tensorflow平台上实现图片生成，与真实图片基本一致。(Through GAN and WGAN, the image generation is realized on the tensorflow platform of python, which is basically the same as the real picture.)

基于二维直方图的最大模糊熵阈值图像分割，相比于一维最大模糊熵，分割效果更好。隶属度函数采用S函数(Based on two-dimensional histogram maximum fuzzy entropy threshold image segmentation, compared to one-dimensional maximum fuzzy entropy segmentation better)

说明：  用水平集做的 MRI大脑图像分割 内有用法说明 （带图形界面）(To do with the level set within the MRI brain image segmentation instructions (with graphical interface))

分割视图 希望大家多多批评指正 多多交流(segmentation View I hope Members can interact more criticism correction)

可以直接调用，适用于各种利用contourlet工具箱做为工具的算法(Can be directly invoked, suitable for various algorithms using contourlet toolbox as a tool)

该源程序实现了SAR图像变化检测的功能，方法是基于修正马尔科夫随机场的模糊C均值实现SAR图像的变化检测。(The source program realizes the function of SAR image change detection. The method is based on modified Markov random field fuzzy C-means to realize SAR image change detection.)

程序功能是把一副倾斜的文字图像矫正过来，通过边缘检测再进行霍夫变换完成(The program function is to correct a skewed text image.)

使用Matlab实现LMS自适应滤波，实现的主要功能有：读入原始音频，加入高斯噪声后用LMS自适应滤波滤除，比较原始音频，加入噪声音频和滤波后音频的区别。(Matlab achieved using LMS adaptive filtering to achieve the main features are: read the original audio, adding Gaussian noise is filtered using LMS adaptive filtering, comparing the original audio, add audio and noise filtered audio difference.)

说明：  SIFT特征点的提取和匹配，SIFT算法首先在尺度空间上进行关键点检测，确定关键点的位置和关键点所处的尺度，然后使用关键点邻域梯度的主方向作为该点的方向特征，最后由关键点邻域梯度信息生成关键点的特征向量(SIFT algorithm firstly detects the key points in the scale space, determines the location of the key points and the scale of the key points, then uses the main direction of the key point neighborhood gradient as the direction feature of the point, and finally generates the feature vector of the key points from the key point neighborhood gradient information)