Image-conversion

用matlab做的基于DCT变换的图像压缩源码，先把图像分成8X8 的子块，然后做DCT变换，再量化，在恢复重建图像(Using matlab to do DCT-based transform image compression source code, first image is divided into 8x8 sub-block, and then make DCT transform, and then quantified, at the resumption of the reconstructed image)

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.)

本代码是使用matlab编写的harr特征的代码，值得大家研究和参考(This code is the use of matlab prepared harr characteristics of the code, it is worth study and reference)

数字图像质量评价量：计算数字图像在基于像素统计差异、基于相似性度量统计差异、基于频谱统计差异以及基于人眼视觉系统统计差异等的图像质量评价量(Digital image quality evaluation of the volume: calculating the number of images in the pixel-based statistical difference, similarity measure based on statistical differences, based on statistical difference spectrum, as well as human visual system based on statistical differences in image quality evaluation of the volume of)

老大中 《变分法基础》，对图像处理很有用的一本书(Lao Dazhong 《basis of variations》 which is very useful in the image processing)

说明：  运动目标识别，采用背景差分方法。背景图像由均值法获得。里面的视频换成前几帧换成纯背景效果能更好一点(Moving target identification, using the background difference method. Background image obtained from the mean. Which replaced the previous video frame replaced by pure background a little better results)

有关图像融合的一个比较全的代码集合，有gui界面，可选择不同的处理图像方法融合图像(the image of a more integrated whole code set, a gui interface choice of different methods of image processing Image Fusion)

The Mathematics of Medical Imaging-The Radon Transform,Back Projection,Discrete Image Reconstruction,The Fourier Transform,Filters and convolutions

说明：  用于遥感图像分类。其输入为几幅遥感图像，使用k-mean聚类方法对图像中的不同地形进行聚类分割(For remote sensing image classification. Their input for a number of remote sensing images, the use of k-mean clustering method to image the topography of the different cluster partition)

说明：  人脸检测算法对Haar特征推进，我们已经改善了大幅利用作为弱分类器的阈值中的累积概率分布点。(A face detector based on VJ algorithm (Boosting on HAAR features). We have improved the performance drastically by using mid cumulative probability distribution point as threshold for weak classifiers. )