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ct扇束等角滤波反投影重建算法matlab源码 分成源图像创建 投影 滤波反投影三个文件(ct Matlab reconstruction algorithm which can be used for CT image reconstruction)

运动目标提取算法，针对多帧图像累加后运动区域的提取(Moving object extraction algorithm for multi-frame image motion region extraction after accumulation)

基于蚁群算法的图像边缘检测，亲测可用，包含四种图像。(Ant colony algorithm based image edge detection, pro test available.It contains four kinds of images.)

中国古代书法作品由于自然风化和人为因素导致的背景中包含大量噪声，重建背景图像，提高观赏价值。直接运行BackgroundFix.m即可(Since the background of ancient Chinese calligraphy works of natural weathering and human factors included a lot of noise, the background image reconstruction, improve the ornamental value. It can run directly BackgroundFix.m)

说明：  完成基于各向异性的自适应超声图像去噪和边缘增强方法，参考Adaptive Anisotropic Diffusion for Ultrasonic Image Denoising and Edge Enhancement.pdf，加入了m参数(The completion of ultrasound-based adaptive anisotropic image denoising and edge enhancement method, reference Adaptive Anisotropic Diffusion for Ultrasonic Image Denoising and Edge Enhancement.pdf, joined the m parameters)

用于矩形检测的源代码，已配说明，易看懂。但不适合于车牌检测等复杂的矩形检测(Rectangular detection for the source code has been assigned that easy to understand. But not suitable for detection of license plate detection rectangular complex)

基于PCL的估算散乱点云的法矢量，并进行颜色渲染显示(estimation of normal vectors for point clouds and color rendering display based on PCL)

利用Matlab编写的基于hough变换的椭圆检测程序，已经运行过的，附图(Written in Matlab, hough transform based ellipse detection procedure has been run off, with photos)

这个文件包里包含了几乎所有的聚类的工具箱，包含采用混合高斯模型的运动物体检测方面的源代码。(This document contains the bag almost all the clustering of the toolbox, including the use of Gaussian Mixture Model of Moving Objects for testing source code.)

说明：  当前论文主要考虑的是非信号依赖的高斯噪声下的图像恢复，本程序实现了泊松噪声下的图像恢复，泊松噪声为信号依赖噪声，能够更加有效逼近实际成像系统噪声。( This is the code that was used in the papers "A Nonnnegatively Constrained Convex Programming Method for Image Reconstruction", "Total Variation-Penalized Poisson Likelihood Estimation for Ill-Posed Problems", "Tikhonov Regularized Poisson Likelihood Estimation: Theoretical Justification and a Computational Method", "An Efficient Computational Method for Total Variation with Poisson Negative-Log Likelihood", "An Analysis of Regularization by Diffusion for Ill-Posed Poisson Likelihood Estimation," "An Iterative Method for Edge-Preserving MAP Estimation when Data-Noise is Poisson", and finally, "Regularization Parameter Selection Methods for Ill-Posed Poisson Maximum Likelihood Estimation". See my publications page for more details. The main algorithm is for nonnegatively constrained, regularized Poisson likelihood estimation. At this point you can choose Tikhonov, total variation regularization, and diffusion regularization. A number of other methods are also implemented. Regularizatio)