leijiayanse

本源码是关于图像盲去模糊的算法，去模糊的几种方法中，盲去模糊目前研究比较热门(This source is about blind image deblurring , several defuzzification methods, blind deblurring research more popular)

对于白噪声的功率谱分析给出了白噪声的频谱波形(White noise matlab )

matlab程序，图像应用处理，可实现散焦模糊篡改图像的定位和分离，实现模糊图像真实性的检测(matlab program, image processing applications can be realized defocus blurred image tampering localization and separation, to achieve authenticity blurred image detection)

说明：  matlab边界自动跟踪程序，该程序可以直接运行，涉及边界判别准则和搜索准则。图像要求为二值图像，可输出为边界的点的坐标和轮廓图像。(matlab boundary automatic tracking program can be run directly involving border criteria and search criteria. Image requirements for binary images can be output for the coordinates of border points and the outline of the image.)

mp算法实现的图像稀疏分解表示 ，已经调试通过，供参考学习(Image fusion based on wavelet transform has been debugged, for reference study)

针对杂波环境下的多个机动目标跟踪问题, 本文将多模型概率假设密度 (Multiple-model probability hypothesis density, MM-PHD) 滤波器和平滑算法相结合, 提出了 MM-PHD 前向 – 后向平滑器. 为了避免引入复杂的随机有限集 (Random finite set, RFS) 理论, 本文根据 PHD 的物理空间 (Physical space) 描述法推导得到了 MM-PHD 平滑器的后向更 新公式. 由于 MM-PHD 前向–后向平滑器的递推公式中包含有多个积分(By integrating the multiple-model probability hypothesis density (MM-PHD) filter with the smoothing al- gorithms, an MM-PHD forward-backward smoother is proposed in this paper for tracking multiple maneuvering targets in clutter. To avoid use of complex random finite set (RFS) theory, the backward updated equation of the MM-PHD smoother can be derived according to the physical-space explanation of the PHD)

用OpenCV进行图像处理与工程开发的一个入门示例程序——Hough变换，效果不错(using OpenCV for image processing and development of a project portal sample program-- Hough Transform good results!)

最小二乘影像匹配，自己2012年8月写的程序，经过测试，内有详细注释，希望对大家有用。(Least Squares Matching write the program in August 2012, has been tested, with detailed notes, and hope to be useful.)

说明：  我是一个图象处理的初学者,用MATLAB语言编写了一些内部的函数,实现了图象处理的功能,比如entropy,laplician,otsu,top-hat等(image processing is a beginners MATLAB language some internal functions, achieving image processing functions, such as entropy, laplician, Otsu, top-hat, etc.)

提取sift特征，通过K均值聚类形成特征包，进行图像检索。(SIFT features are extracted and image packets are retrieved through K mean clustering.)