image-information-hiding

说明：  该文件主要用于减少相干斑噪声，包含各种常见的滤波器如Lee滤波器，Frost滤波器等(This file is mainly used to reduce speckle noise, including a variety of common filters, such as Lee filter, frost filter and so on)

说明：  行人检测HOG+SVM进行图片中行人检测，提供训练用的pos和neg样本，效果还可以；没有SVM工具箱的，压缩包里已经提供了，安装一下即可(Pedestrian detection HOG + SVM for pedestrian detection in pictures, providing POS and neg samples for training, the effect is good; without SVM toolbox, the compression package has been provided, just install it.)

分形图像压缩 效果好 但是运行时间长 需要改进(Fractal image compression effective running a long time but need to improve)

对模糊图像进行复原（代码中样例）后，进行ISNR值的计算。由于复原后图像和原图像可能存在对齐问题，而该对齐问题不影响复原结果的主观效果，因此该代码首次考虑这一问题并给出相应的图像对齐及ISNR计算方法。(Restoration of blurred images (sample code), the calculation ISNR values. Due to possible alignment problems and the restored image after the original image, and the alignment problem does not affect the results of the subjective effects of recovery, so the code for the first time to consider this issue and the corresponding image alignment and ISNR calculation.)

数字图像处理，包括图像的卷积，相关，线性滤波处理，边缘检测都是运用labview来完成的。(Digital image processing, including images, linear convolution, filter, edge detection is accomplished using labview)

该程序实现了相位编组法检测，可以将灰度缓慢变化的边缘提取出来(Realize the process of phase formation assay can be gray-scale changes in the edge of the slow extracted)

c#数字图像处理算法典型实例,本资料从数学角度来讲解图片处理的各种方法，是个不可多得的实例(c# a typical example of the digital image processing algorithms, the data from a mathematical point of view to explain the various methods of image processing, is a rare instance)

基于亚像素运动估计算法的块匹配运动，有源码跟参考文献，初学的小伙伴可以看看(Block-matching motion estimation algorithm based sub-pixel motion, there is source code with references, beginner can look at a small partner)

此压缩包包含了数字图像处理算法的基本M函数和ICE界面定制的M函数。(This package includes the digital image processing algorithms of the basic M function and ICE interface customized M function.)

本文首先概述了独立成分分析与盲信号分离， 主成分分析的关系， 然后系统地介绍了该技术的基本概念和数据模型， 对现今比较流行的各种独立成分分析算法进行了剖析和总结。最后对独立成分分析的发展及应用前景作了展望(In this paper, an overview of independent component analysis and blind signal separation, principal component analysis of the relationship, and then systematically introduces the basic concept of the technology and data models for today' s more popular variety of independent component analysis algorithm is analyzed and summarized. Finally, independent component analysis of the development and application prospects are described)