NESTA_v1.0

小波算法。机械化的提高我们的效率。让我们减少错误率。大家要对她有性心。(Wavelet algorithm. Mechanization to improve our efficiency. Let us reduce the error rate. We have to have sex to her heart.)

各种边缘检测程序，包括2Sobel算子边缘检测， Prewitt算子边缘检测，Roberts算子边缘检测， Laplace算子边缘检测，Canny算子边缘检测 (Edge Color)

halcon图像处理软件用来检检测图像的圆状物体。可直接使用。 (halcon image processing software is used to check the oblong object detection image. Can be used directly.)

说明：  运用MATLAB，软件结合模板匹配原理进行的英文字符识别系统(English character recognition system based on MATLAB and template matching)

图像修复是图像处理的一个重要问题。这是一个困难的问题，同时填写在地区缺少图像信息的纹理和结构。以损坏的图像inpaint失踪的结构和纹理信息，基于小波分解的图像修补算法提出。首先损坏的图像分解成子图像和纹理结构，利用小波子图像转变。然后，地区结构中缺少的信息的子图像重建曲率驱动扩散（CDD），算法，而在同一区域的纹理子图像填写基于改进的纹理合成的典范 最后，恢复的图像，通过重组，结构和纹理恢复的结果。大量的实验表明，该算法可以快速，高效地恢复在同一时间的结构和纹理信息，视觉效果和峰值信噪比比（PSNR）比同类算法更好。(Image inpainting is an important problem image processing. It is a difficult problem to simultaneously fill-in the texture and structure in regions of missing image information. In order to inpaint the damaged image with both missing the structure and texture information, an image inpainting algorithm based on wavelet decomposition is presented. First the damaged image is decomposed into structure sub-image and texture sub-image using the wavelet transformation. Then, the sub-image with the region of missing information in the structure is reconstructed by Curvature-Driven Diffusions (CDD) algorithm, while the same region in the texture sub-image is filled-in with the improved texture synthesis based on exemplar Finally, the restored image is given by recombining the structure and texture restored results. A large number of experiments show that the proposed algorithm can quickly and efficiently restore the structure and texture information at the same time, and the visual eff)

经典的基于距离变换的图像细化的骨架提取方法。(Classic based on distance transform refining skeleton extraction method. )

利用非局部均值的算法对图像做去噪，速度比较快，但效果一般，存在一定局限性(Using non-local mean algorithm to denoise image is faster, but the effect is general and there are some limitations.)

基于先验信息和射影几何变换的车牌图像矫正 对于图像矫正还是有帮助的(License plate image correction based on a priori information and projective geometry transform for image correction or help)

特征点匹配时，采用构造KD-tree，用最优节点优先法寻找最佳匹配点(Matching feature points, the tectonic KD-tree, with the optimal node is preferred method to search for the optimal matching point)

说明：  PCA Kmeans-master图像变化检测算法设计(ChangeDetectionPCAKmeans-master)