Digital-Image-Processing-writed-by-Gonzalez-secon

矩阵 pca或者低秩方法去噪，利用svd分解，实现对图像矩阵的去噪，该方法支持对rgb图像的去噪。使用代码请 文章中表明出处，感谢。 感谢重庆市研究生科研创新项目支持，项目号CYS16183(image denoise by low-rand regularizer or pca method. the low rank is evaluted by svd, and this method is also support for rgb image.)

聚束SAR极坐标格式算法，包括dechirp处理以及距离向插值和方位向插值(Spotlight SAR polar format algorithm, including dechirp processing and distance and azimuth to the interpolation Interpolation)

应用傅立叶变换对图像处理，实现查看图像的功率谱，以及功率谱经过巴特沃斯高通、低通和高斯高通、低通滤波器后图像的功率谱，再把功率谱还原为图像的功能。(Fourier transform for image processing, see the image of the power spectrum, and the power spectrum after Butterworth high-pass, low-pass and high-pass Gaussian low-pass filter of the image after power spectrum, and then restore the power spectrum of the image.)

陷波滤波器，可调节陷波频域位置，可调节陷波大小，函数，可直接调用，由高斯，理想，巴特沃斯组成。(notchfilter, which is suitable for digital image processing in frequency domain. Notch filter, adjustable notch frequency domain position, adjustable notch size, function, can be called directly, composed of Gauss, ideal, Butterworth.)

说明：  利用特征提取和分类的方法,假设输入值为{大,圆,光滑}为水果的三个特征值,来代表三种不同的水果,识别苹果\梨\香蕉\桔子\菠萝水果类别. (The use of feature extraction and classification methods, assuming that the import value was (large, round, smooth fruit) for the three characteristics of value, to represent three different types of fruit, to identify Apple \ pear \ banana \ orange \ pineapple fruit category.)

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 )

图像修复算法 使用criminisi算法对破损图像进行修复。(The image restoration algorithm uses criminisi algorithm to repair the damaged image.)

多目标跟踪算法SORT开元代码，实现实时在线跟踪(Multi target tracking algorithm SORT code, Real time online tracking)

Tsai标定算法，用用于图像标定的，可以使用 (Tsai calibration algorithm used for image calibration, you can use)

mp算法实现的图像稀疏分解表示 ，已经调试通过，供参考学习(Image fusion based on wavelet transform has been debugged, for reference study)