NSCT

说明：  小波分析增强滤波。小波变换的原理类似傅里叶变换，只是把三角函数基换成了小波基。与傅里叶变换不同，小波变换有两个变量：scale和translation。scale控制小波函数的收缩，其导数即为频率，translation控制小标函数的平移，平移量对应时间。通过信号的伸缩平移，可以得到某种重合情况，这样积分也会得到一个极大值，不同的是，得到频率成分的同时，还可以知道该频率的时间位置。(Wavelet analysis enhances filtering.The wavelet transform works like the Fourier transform, except that the trigonometric basis is replaced by a wavelet basis. Unlike the Fourier transform, the wavelet transform has two variables: scale and Translation. Scale controls the contraction of the wavelet function, its derivative is frequency, translation controls the translation of the small scale function, and the translation corresponds to the time. By stretching and shifting the signal, you can get some kind of coincidence, so that the integral will also get a maximum, but the difference is that when you get the frequency component, you can also know the time position of that frequency.)

document about computer vision - image processing

关于图像配准的经典外文文献，影响因子很高(on the registration of foreign classic literature, high impact factor)

moravec算子提取特征点，然后用相关度进行匹配(moravec operator feature point extraction, and then match the correlation)

@Wiener filter for Skull X-Ray images

huffman编码 基于对BMP格式图像的编码！ 具体你们自己看看！(Huffman code)

用于图像检测的正态分布计算的c代码实现，现在将这个函数写在头文件中了(Normally distributed computing for image detection of the c code, this function will now be written in the header file in the)

图像增强效果评价指标：1.欧拉范数误差指标 2.等效视数 3.边缘保持指数 4.对比度增强指数(Evaluating results of image enhancement: 1. Oula Fan error index number 2. Equivalent number of 3. Edge-preserving index 4. Contrast enhancement index)

包含基于三种不同方法的应用于图像处理的矩阵低秩分解的matlab算法，可以将目标图像分解为一个低秩图像和一个稀疏图像之和；并包含一种基于区域生长方法的图像区域识别程序，可以用来提取图像中的目标。(Containing used in image processing based on three different methods of low-rank matrix decomposition algorithm matlab, the target image can be decomposed into a low rank of the image and the image and a sparse and contains a region growing method based on image area recognition program It can be used to extract the object in the image.)

信号处理的小波导引：稀疏方法（原书第3版） 《信号处理的小波导引：稀疏方法（原书第3版）》全面论述了稀疏表示的重要概念、技术和应用，反映了该主题在当今信号处理领域所起的关键作用。作者在解释了稀疏的主要概念后将其运用于信号压缩、噪声衰减和逆问题，同时给出了冗余字典、超分辨率和压缩感知中的稀疏表示。 　　本书既可以让应用数学系的学生了解数学公式的工程意义，也可以让计算机及电子工程系的学生了解工程问题的数学描述。对于小波理论与应用的研究人员，本书更是一本极具价值的参考书。(a Wavelet Tour of Signal Processing(Third Edition.The Sparse Way))