Floyd-Steinberg

在工程运用中，我们往往在卡尔曼滤波器后端连接平滑滤波器构成平滑卡尔曼滤波器，来加强滤波性能的改善，程序通过简单输入信号 在增加随机观测噪声情况下 在平滑卡尔曼滤波前后进行观测对比，可得平滑卡尔曼滤波性能得到了改善。(In engineering application, we often back-end connections in the Kalman filter Kalman smoothing filter the smoothing filter, to enhance filter performance improvement program by simply increasing the input signal in the case of random measurement noise Kalman filter before smoothing contrast observation can be obtained Kalman smoothing performance is improved.)

基于主成分分析的遥感图像融合算法，融合结果质量较好。(The fusion algorithm of remote sensing image based on principal component analysis has better fusion result.)

一种新颖的退化图像复原算法。结合最大差值先验假设和最大局部对比度先验假设，快速有效的得到退化图像的深度转换估计图。结合软抠图法和shades of the gray算法实现图像去雾和水下图像处理。(A novel degraded image restoration algorithm. Combined with maximum difference between a priori assumptions and maximum local contrast priori hypotheses, quickly and efficiently get degraded image depth conversion estimates Chart. Combined with soft matting law and shades of the gray image algorithm to image processing and underwater fog)

基于地物分类真值的高光谱遥感图像分类精度评价(Accuracy uation of remote sensing image classification)

经过改进的快速的双边滤波器算法，主要是先下采样，然后进行滤波，最后再进行上采样(Improved bilateral rapid filter algorithm is the first major down-sampling, and then filtered, and finally to the sampling)

image decomposition and reconstruction

基于区域的空间域图像融合。先对源图像作小波分解，低频分量加权平均，高频分量用模糊C均值聚类算法进行区域分割，对区域进行基于ssim值的融合，最后小波逆变换得到融合图像。(region based spatial domain method.First do the source image wavelet decomposition low frequency components weighted average of the high frequency component using Fuzzy C-means clustering algorithm region segmentation of regional integration based ssim value, and finally fused image inverse wavelet transform)

图像的特征用到了Dense Sift，通过Bag of Words词袋模型进行描述，当然一般来说是用训练集的来构建词典，因为我们还没有测试集呢。虽然测试集是你拿来测试的，但是实际应用中谁知道测试的图片是啥，所以构建BoW词典我这里也只用训练集。 其实BoW的思想很简单，虽然很多人也问过我，但是只要理解了如何构建词典以及如何将图像映射到词典维上去就行了，面试中也经常问到我这个问题，不知道你们都怎么用生动形象的语言来描述这个问题？ 用BoW描述完图像之后，指的是将训练集以及测试集的图像都用BoW模型描述了，就可以用SVM训练分类模型进行分类了。 在这里除了用SVM的RBF核，还自己定义了一种核： histogram intersection kernel，直方图正交核。因为很多论文说这个核好，并且实验结果很显然。能从理论上证明一下么？通过自定义核也可以了解怎么使用自定义核来用SVM进行分类。(Image features used in a Dense Sift, by the Bag of Words bag model to describe the word, of course, the training set is generally used to build the dictionary, because we do not test set. Although the test set is used as the test you, but who knows the practical application of the test image is valid, so I am here to build BoW dictionary only the training set. In fact, BoW idea is very simple, although many people have asked me, but as long as you understand how to build a dictionary and how to image map to the dictionary D up on the line, and interviews are often asked me this question, do not know you all how to use vivid language to describe this problem? After complete description of the image with BoW, refers to the training set and test set of images are described with the BoW model, the training of SVM classification model can be classified. Apart from having to use the RBF kernel SVM, but also their own definition of a nuclear: histogram intersection kernel, histogram )

说明：  matlab GUI 界面，全息图重建，通过改变Z和lamada可以得到-1级，0级，1级再现图(Matlab GUI interface, hologram reconstruction, by changing Z and lamada can be - 1, 0, 1 level reproduction map)

压缩文件是CNN卷积神经网络图像处理的数据库mnist_uint8.mat(Compressed file is the database of CNN convolution neural network image processing mnist_uint8.mat)