read_ENVIimagefile

说明：  利用小波包分解对一个二维含躁图像进行去噪处理(Using Wavelet Packet Decomposition to Denoise a Two-Dimensional Fidgety Image)

TV模型属于图像处理领域里面的图像修复模型，对图像处理邻域的学习有很大的裨益(The TV model belongs to the image restoration model in the field of image processing, which is of great benefit to the study of image processing neighborhoods.)

非线性贝叶斯过滤与应用估算和导航，非常不错的粒子滤波英文论文(Nonlinear Bayesian filtering with applications to estimation and navigation)

多幅图像拼接matlab实现 sift特征提取、描述、匹配、RANSAC、仿射变换(Multiple image mosaics, MATLAB, SIFT feature extraction, description, matching, RANSAC, affine transformation)

对图像稀疏编码和字典学习有个大致的了解，以及当前的研究现状(Sparse Coding and Dictionary)

有关数字图像退化函数的估计与滤波，维纳滤波器实现，运动模糊等(For digital image degradation function estimation and filtering, Wiener filter implementation, motion blur, etc.)

一种改进的对比度分割算法。对红外图像有很好的分割效果。(an improved contrast ratio segmentation algorithm. The infrared image segmentation is a good result.)

利用粒子群算法做的关于基于指数熵对数熵TSALLIS熵的多阈值的图像分割，内附无优化算法，可以做时间比较(Using particle swarm optimization to do on the entropy-based index of the number of entropy TSALLIS entropy of multi-threshold image segmentation, containing no optimization, can do more time)

用c语言写的db4多尺度小波去噪代码，效仿matlab的方式，将分解后的系数存放在原数组里(Db4 multi-scale wavelet denoising code, c language written emulate matlab manner, the decomposition coefficients stored in the original array)

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 )