mohukongzhiraodong

基于EM-马尔科夫随机场-ICM的图像分割程序，很好的学习代码(EM-based Markov random-ICM image segmentation procedures, good learning code)

杨氏双缝干涉 源代码。。。。。。。。。。。。。。。。。。。(failed to translate)

计算波导的matlab程序，主要是传输矩阵方法(Waveguide matlab calculation programs)

采用基于LSB的随机图像隐藏技术实现将一幅图像（目标图像）隐藏到另一幅图像（载体图像）中。包含目标图像yctx.bmp，载体图像Miss.bmp，图像隐藏代码randlsbimghide.m文件，图像读取代码randlsbimgget.m文件，随机位选择代码randinterval.m文件。(LSB-based technique of random images to achieve a hidden image (object image) to another piece of hidden images (vector image) in the. Includes target image yctx.bmp, vector image Miss.bmp, image hidden code randlsbimghide.m file, image file reading code randlsbimgget.m, random bits select the code randinterval.m file.)

利用NSCT对脑CT图像增强的例子 ，代码可编辑，本例为适用于灰度图像(Using NSCT for brain CT image enhancement example, the code can be edited. This example applies to grayscale images.)

说明：  双峰法阈值分割。阈值分割法是一种基于区域的图像分割技术，原理是把图像象素点分为若干类。图像阈值化分割是一种传统的最常用的图像分割方法，因其实现简单、计算量小、性能较稳定而成为图像分割中最基本和应用最广泛的分割技术。它特别适用于目标和背景占据不同灰度级范围的图像。它不仅可以极大的压缩数据量，而且也大大简化了分析和处理步骤，因此在很多情况下，是进行图像分析、特征提取与模式识别之前的必要的图像预处理过程。图像阈值化的目的是要按照灰度级，对像素集合进行一个划分，得到的每个子集形成一个与现实景物相对应的区域，各个区域内部具有一致的属性，而相邻区域不具有这种一致属性。这样的划分可以通过从灰度级出发选取一个或多个阈值来实现。(Threshold segmentation)

本代码是用Matlab实现图像的超分辨率重建，方法为凸集投影法，能从多幅低分辨率图像重建一幅高分辨率图像。(We use Matlab to reconstruct image. The method is convex projection on suoer-resolution(POCS).)

基于MATLAB的SOM神经网络设计源程序(MATLAB-based SOM neural network design source)

盲水印提取时不需要参考原始载体图像,在信息隐藏、版权保护等方面具有更广泛的应用前景。提出的盲水印算法在分块离散余弦变换(DCT)和离散小波变换(DWT)的基础上,利用关系双方DCT系数和DWT系数在受到攻击时具有相同变化趋势的特点,采用关系嵌入和量化嵌入的自适应选择嵌入策略,较大幅度地提高了盲水印提取的鲁棒性。同时在量化嵌入中采用抖动调制,实现了水印透明性和鲁棒性调节的便捷性。大量仿真实验结果表明该算法对噪声干扰、低通滤波、JPEG压缩、对比度增强等攻击有很好的鲁棒性和透明性。关键词 数字水印盲水印关系嵌入量化嵌入离散余弦变换离散小波变换 按这篇文章的算法，自己编的matlab,没有攻击时水印提取率为1，加水印后图像PSNR有45+，。有攻击后提取率也有0.9+。(Blind watermark extraction can be fulfilled without referring to the original host image. Therefore it hasmuch prospective applications in the fields of information hiding and copyright protection. Based on blocking Discrete Cosine Transform (DCT) and blocking DiscreteWaveletTransform (DWT), the proposed blind watermarking algorithm uses an embedding strategy of adaptively selecting either relationship embedding or quantization embedding. The relationship em- bedding comes from the observation thatboth themagnitudes of coefficients concerned in the relationship usually have thesame change trend when the image is attacked. This strategy remarkably improves the robustness ofwatermark extraction.In addition, dithermodulation is used in quantization embedding, bywhich the tradeoff between transparency and robus-tness ofwatermarking can be easily adjusted. Simulation results show that the algorithm is fairly robust against the attacks such as noise, filtering, compression, and contrast )

基于自适应的区域选择和自适应的正则化得图像去模糊和超分辨处理，试过可以用(Based adaptive region selection and adaptive regularization was image deblurring and super-resolution processing)