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Transferring color to greyscale images的原文及源代码，包括详细的测试图像以及相应的论文及源代码，是学习图像色采转移必看的经典内容。(Transferring color to greyscale images of the original text and source code, including detailed test image and the corresponding papers and source code is studying the image to see the classic color adopted transfer content.)

tiff图像的读取与处理，并且说明清新，有很大的帮助。(The reading and processing of the tiff image, and the description of the fresh, is of great help.)

提出了一种基于非下采样Contourlet 变换的自适应图像去噪方法 首先对噪声图像进行非下采样Contourlet 变换，得到各 个尺度各个方向子带的系数，再根据该系数的能量自适应地调整去噪阈值 实验表明，与Contourlet 多尺度阈值去噪 Contourlet 自 适应阈值去噪相比，该方法在保留图像边缘细节的同时，能提高图像的PSNR 值，减少了Gibbs 现象(A new adaptive method of image de-noising based on NonSubsampled Contourlet Transform （NSCT）is presented.Firstly， the noisy image is decomposed into a set of multiscale and multidirectional frequency subbands by NSCT， and then the threshold is adapted automatically according to the energy of subband coefficients.Compared with the multi-scale threshold using contourlet transform and using adaptive threshold based on contourlet transform， the simulation results show that the performance of this method is superior in PSNR， meanwhile it can maintain the image edge and reduce the Gibbs phenomen)

通过Matlab实现维纳滤波及逆滤波复原图像 不使用matlab自带函数 而是自编程序实现(Matlab to achieve through the Wiener filter and inverse filter to recover the image does not use function but rather bring their own self-matlab program)

compressing the image using JPEG 2000 compression coding to get 10:1 ratio

PCA+SVM，对图像进行降维分类，并在yale库上测试取得比较好的效果(PCA+ SVM, dimensionality reduction of image classification, and yale library to achieve better test results)

说明：  adpmedfilt3.m为自适应中值滤波器函数； adpmedfilt3_test.m为利用上述自适应中值滤波器函数进行中值滤波，同时与普通中值滤波器滤波结果对比； 其余为基础图像处理函数； image文件中为实验图像kl.jpg；(adaptive median filter to filt images)

说明：  matlab实现图像的锐化，使用不同的算子分别对图像进行运算(Matlab achieve sharper images, the use of different operators were right image Operational)

图像质量评价代码： 灰度平均梯度 边缘能量 方差 点锐度(image quality evalution method)

用PHD滤波的方法实现多目标跟踪，及对PHD滤波性能的检测。(PHD filter method using multi-target tracking, and performance testing for the PHD filter.)