all-the-recovery-algorithm

在统计学中，皮尔逊积矩相关系数（英语：Pearson product-moment correlation coefficient，又称作 PPMCC或PCCs[1], 文章中常用r或Pearson s r表示）用于度量两个变量X和Y之间的相关（线性相关），其值介于-1与1之间。在自然科学领域中，该系数广泛用于度量两个变量之间的相关程度。它是由卡尔·皮尔逊从弗朗西斯·高尔顿在19世纪80年代提出的一个相似却又稍有不同的想法演变而来的。这个相关系数也称作“皮尔森相关系数r”。(In statistics, Pearson product-moment correlation coefficient (English: Pearson product-moment correlation coefficient, also known PPMCC or PCCs [1], the article used or Pearson s r r representation) is a measure of the two variables X and Y between correlation (linear correlation), a value between-1 and 1. In the natural sciences, the coefficient is widely used to measure the degree of correlation between the two variables. It is made a similar Karl Pearson Francis Galton in the 1880s but made a slightly different idea evolved. The correlation coefficient is also called Pearson s correlation coefficient r .)

说明：  用C语言实现图像的平滑，锐化及双线性内插算法的实现，效果很好(Using C language to achieve image smoothing, sharpening and bilinear interpolation algorithm to achieve good results)

Lee滤波器，具有很好的去噪效果，结合各种小波，可以有效去除SAR图像噪声。(Lee filter, has a good de-noising effect of combination of wavelet can effectively remove the SAR image noise.)

源码包括了常用的图像处理方法，代码详细，适合初学者(Source code, including the commonly used image processing method, the code in detail, suitable for beginners)

Image processing functions: Gamma correction Contrast stretching histogram equalization

说明：  计算图像对应的各阶原点矩，中心矩，以及Hu不变量,输入是图像和矩的阶数。(Image raw，Central，and Hu s moments computation)

基于马尔科夫随机场算法(MRF)的图像分割(Image segmentation of Markov random field algorithm)

序列DInSAR技术处理过程，相位在空间维上解缠的MATLAB实现。(Sequence DInSAR technique process, phase d turned over in the space of MATLAB.)

说明：  自己编写的基于直方图均衡化以及边缘增强的程序！(I have written based on histogram equalization and edge enhancement procedure!)

Retinex算法对较暗区域的图像处 理有明显效果,改进的Retinex方法则是基于经典Retinex,对具有较高亮度的遥感图像通过图像变 换方法达到去除模糊的目的。 (Retinex algorithm for image processing on the dark region was effective to improve the Retinex method is based on the classic Retinex, on remote sensing images with high brightness achieved by an image transformation method aims to remove ambiguity.)