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Matlab源程序代码，著名的PSO程式代码，用于拟合超二次曲线，曲面的代码，适用于粒子系统。呵呵，与大家一起分享受(Matlab source code, the famous PSO program code for the quadratic curve-fitting, Surface code applicable to the particle system. Oh, and that they have enjoyed.)

说明：  压缩感知的一个恢复算法，规则化正交匹配追踪(A recovery algorithm for compressed sensing, rules-based orthogonal matching pursuit)

数字图像处理radon+matlab变换算法代码(Digital image processing algorithms transform radon+ matlab code)

用matlab软件编写的图像配准代码，用于图像配准或图像处理(matlab edge detect-matlab edge detect)

说明：  BMED二维经验模态分解，用于处理二维信号(Two dimensional empirical mode decomposition)

用差分盒维数处理图像，通过对图像分块，然后每一块来计算该区域的分维数，将其作为该区域所有像素上每一像素的分维数。然后，增强分维数分布图效果，然后取阈值，最后，给人造目标加框。(Differential box counting process the image, the image block, and then every one to calculate the fractal dimension of the region, as all the pixels in the region of each pixel on the fractal dimension. Then enhanced fractal dimension distribution effect, and then take the threshold, and finally, to the man-made objects framed.)

这是运用matlab进行图像处理时会遇到的基本操作，包括图像的代数运算、几何运算、空间变换、区域操作等(This is for image processing using matlab will encounter the basic operations, including images of algebra, geometry operations, space transformation, regional operations, etc.)

图像恢复：快速非局部均值滤波--A.Buwades等人提出的非局部均值滤波的快速实现方法(Image restoration: the proposed fast non-local means filtering- A. Buwades non-local means filtering Fast Implementation)

针对CT 医学图像和MR I 医学图像成像特点, 提出了基于快速整数提升小波变换的融合方法。在CT 和 MR I 两幅医学图像配准的前提下, 利用提升小波变换把图像分解成低频和高频子图像, 对于小波变换后的高频 子图像, 选择区域标准差大的作为融合后的子图像 对于低频子图像, 采用加权融合, 最后进行小波逆变换, 得到 融合后的图像, 并对融合后图像用信息熵、平均梯度、相关系数的指标进行评价。实验结果表明, 基于快速整数提 升小波变换融合中, 小波高低频系数采用不同的规则能够取得更好的融合效果, 其轮廓清晰。该算法能够提升融 合后的医学图像信息量, 同时有效地保护图像的细节信息, 在执行时间和图像质量上均优于普通小波算法。(A imed at the characterist ics of CT andMR Imedical images, a new image fu sion al2 go rithm based on the fast in t lif t ing w avelet t ran sfo rm s is p ropo sed. Two o riginal images are decompo sed by the fast in t lif t ing w avelet t ran sfo rm s. The fu sion ru le based on the max imum standard deviat ion value variance is u sed to fu se the h igh f requency sub2image, w h ile a w eigh t2 ed average fu sed ru le is app lied by coeff icien t s of the low f requency. F inally, the fu sion image is recon st ructed fo r perfo rm ing inverse fast in t lif t ing w avelet t ran sfo rm s. The fu sion image is evaluated by en t ropy, average gradien t, and co rrelat ion coeff icien t s. Experimen tal resu lt show s that the fu sion image hasmo re info rmat ion than o riginal images and imp roves the quali2 ty of o riginal images. M eanw h ile, the fu sion image p ro tect s detail characterist ics of the image, thu s the real2t ime p rocess and image qualit ies are w ell than tha)

Isoperimetric Graph Partitioning for Image Segmentation