Colony_partition

关于speckle 去噪的一种很新的方法，基于流行的SRAD方法改进的。(The reference of thic package is On the Estimation of the Coefficient of Variation for Anisotropic Diffusion Speckle Filtering)

偏微分方程图像复原代码，其中有频域迭代盲解卷积H1模型，TV去噪模型，自己编的自适应去噪模型，以及改进的自适应去噪模型，令附卫星图片，PSNR代码(Image restoration code of partial differential equations, including iterative frequency-domain blind deconvolution H1 model, TV denoising model, made themselves adaptive denoising model, and improved adaptive denoising model, so attached to satellite images, PSNR code)

图像分割 基于区域的分离和合并 实例 自己写的(SPLITMERGE Segment an image using a splil-and-merge algorithm.)

MATLAB程序：利用DWT的图像数字水印的嵌入与提取，里面包含水印图像、原图像。(MATLAB programs: the use of DWT image watermarking embedding and extraction, which contains a watermark image, the original image.)

说明：  利用其中不同的光谱匹配算法实现光谱匹配，内部还含有相关数据(Using different spectral matching algorithms to achieve spectral matching, there are also relevant data inside)

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 )

通过将高分辨率的图像和低分辨率的图像进行融合，来实现图像的融合。(By fusing high-resolution and low-resolution images, image fusion can be achieved.)

说明：  利用梯度算子对图像边缘进行计算，比较得出清晰度评价值，进行取得最佳清晰度图片(The gradient operator is used to calculate the edge of the image, and the sharpness evaluation value is obtained by comparison to obtain the best sharpness image)

全局法图像垂直条纹去除程序。可以处理高光谱图像、RGB图像，注释详尽。对于倾斜的条纹噪声图像，需要西安进行旋转，再使用该程序去除。内涵循环低速版和无循环高速版，通过比较（处理高光谱数据），高速版的速度比低速版快上近20倍。(Global image vertical stripes removal procedures. Can process the hyperspectral image, RGB image, detailed notes. The stripe noise images of Xi an, need to rotate, then use the program to remove. Connotation of circular low version and loop free high speed version, by comparing (hyperspectral data processing), high speed rate nearly 20 times than the low-speed model.)

图像融合技术，对可见光图像与红外图像进行融合。(Image fusion, image visible and infrared image fusion.)