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matlab代码实现的retinex图像增强(retinex image enhancement matlab code)

说明：  图像去噪，降低图像的噪声，提高图像PSNR(image denoise,improve the psnr of image)

详细介绍了K-SVD字典训练的详细过程，对稀疏表示研究很有帮助.(Detailed introduces the K- SVD dictionary of detailed training process, the sparse say that research to have the help very much. )

这是一个用于血管分割的程序，程序中附带多幅测试图片(This is a program for vascular segmentation, with multiple test images attached to the program)

较简易的视频目标跟踪算法，有需要的朋友可参考(Than simple video target tracking algorithm, there is a need to refer to a friend)

采用radon变换检测直线，有详细注解，具有抗旋转性(Radon transform line detection, and a detailed explanation, with anti-rotational)

本文主要研究基于水平集方法的活动轮廓模型图像分割，在回顾活动轮廓模 型发展的基础上，介绍了曲线演化理论及其水平集方法，证明了水平集方法可以 有稳定的数值实现方式且其处理拓扑变化的自然性，进一步引出了变分水平集方 法。 本文介绍了两种不同的几何活动轮廓模型：基于梯度信息的李纯明模型以及 基于区域信息的 Chan-Vese 模型（C-V 模型）。在分析上述两模型的优缺点上，提 出了一种改进的 C-V 模型，改进模型引入了距离约束项，同时对基于区域的外部 能量项进行了改进，使得水平集函数重新初始化寓于模型的演化之中，分割结果 对参数的依赖性减小。 (This paper mainly study image segmentation of active contour model based on level set. After reviewing the development of the active contour model, curve evolution and level set method are presented, then we proved that the level set method has a stable numerical realization and can describe the topology change of the contour naturally, in addition we introduce level set of variational method. We introduce two different GAC models in this paper: Li Chunming model which is based on gradient information and Chan-Vese model （C-V model） which is based on region information. Based on the drawbacks and advantages of these two models, we improve the C-V model by adding penalizing energy term of signed distance function, at the same time, mending the external energy term which based on region information, then the re-initialization of the level set can accomplish in the model evolution, and the degree of the segmentation result depend on the parameters decline. )

遥感图像除云软件，原理是图像同时期替换掉有云区域，使用IDL语言编写(remove cloud from remote sensing image)

通过边缘检测寻找图像上圆点的圆心坐标，可用于大量的图像处理程序(Image through edge detection to find the center coordinates of the dots)

主成分分析方法（PCA），PCA算法的理论依据是K-L变换，通过一定的性能目标来寻找线性变换W，实现对高维数据的降维。(Principal component analysis (PCA), PCA algorithm is based on the theory of KL transform, through a certain performance targets to find the linear transformation W, the realization of high-dimensional data, dimensionality reduction.)