PCNN

说明：  此代码是由FCM算法改变的且用于图像分割，比FCM算法对图像的分割的效果更好(This code is changed by FCM algorithm and is used for image segmentation, which is better than FCM algorithm for image segmentation)

说明：  自己上课编写的NCC sad图像匹配的代码(NCC sad image matching code prepared in class)

曲波变换的matlab图像处理工具箱。用于图像处理及增强(contourlet_toolbox,for image processing and enhancement)

用高斯模型算法来处理视频，提取前景信息，适合动态背景(Gauss model algorithm is used to process video and extract foreground information, which is suitable for dynamic background)

说明：  VC++数字图像处理典型算法及实现 01 源程序(VC++ Digital Image Processing Algorithm and Implementation of a typical 01)

主要是一个小的图像处理程序,是将一个模糊的图像进行复原的程序(It is mainly a small image processing program.)

图像融合工具包：包含多种基本图像融合方法 开发环境：matlab(Image Fusion tool kits : a mixture of basic image fusion method development environment : Matlab)

一种用于稀疏表示求解L1范数的算法，用L1范数近似L0范数获得了更好的效果。(A method for sparse representation algorithm for solving the L1-norm)

SAR图像目标检测是SAR ATR(自动目标识别)的关键步骤,也是近年来SAR图像解译应用的一大研究热点.在广泛文献调研的基础上,本文从SAR图像目标检测的历史沿革、研究现状开始,综述了SAR图像目标检测的研究进展及存在问题(SAR image target detection is the SAR ATR (automatic target recognition) a critical step in SAR image interpretation is also applied in recent years, a major research focus. In the extensive literature on the basis of research, this article from the SAR image target detection to the history and study of the status quo began an overview of SAR image target detection Progress and Problems)

说明：  本文以k-means算法为背景，引入信息熵相关知识，从而实现全自动分割图像。然而在利用混合高斯模型对图像进行数据分析时，会发生一定的过拟合现象，导致我们得不到预期的聚类数目。本文设计合理的合并准则，令模型简化，有效地消除过拟合现象，使得最后得到的聚类数目与预期符合。，设计合理的准则改进了图像的全自动分割方法，使得分割结果更加优化(In this paper, k-means algorithm is used as the background, and information entropy related knowledge is introduced to realize full-automatic image segmentation. However, when the Gaussian mixture model is used to analyze the image data, there will be some over-fitting phenomenon, resulting in that we cannot get the expected number of clusters. In this paper, a reasonable merging criterion is designed to simplify the model and effectively eliminate the over-fitting phenomenon, so that the final clustering number is in line with the expectation. A reasonable criterion is designed to improve the automatic image segmentation method and make the segmentation result more optimized.)