06362230

说明：  二自由度车辆动力学模型(Simulink)(Two-degree-of-freedom vehicle dynamics model)

用SLM法降低PAPR的CCDF,调用SLM函数来实现，毕业设计很有用的源代码(SLM method used to reduce PAPR of the CCDF, call the SLM function to achieve graduation design of the source code is useful)

matlab关于DCT的图像处理程序 供大家交流(Matlab on DCT image processing procedures for the exchange of)

这个算法包实现了从多幅拥有共同结构的图像里提取出共同形状的骨架。( This package implements the algorithm for Sketching the Common as described in: Shai Bagon, Or Brostovsky, Meirav Galun and Michal Irani "Detecting and Sketching the Common", CVPR 2010)

线性非局部均值滤波器用于图像处理中的去除噪声平滑图像，能够较好的保持图像的边缘(Linear non-local mean filter for image processing to remove noise smoothing of images, to better maintain the image edge)

用对偶分裂算法处理乘性噪声如盐椒噪声（L1+TV），参数由电脑自动选取，matlab程序(A Duality-Based Splitting Method for L1-TV Image Restoration with Automatic Regularization Parameter Choice)

说明：  针对弱小目标信号提出的RXD异常检测算法，实现对目标的检测(A RXD anomaly detection algorithm for dim and small target signals is proposed to realize target detection.)

transver mat file format to raw file format, simply matlab code, usd to 3D medical image processing/data input

对于原始图像 < f5.tif > ，实现下列结果。 a.用平均滤波器平滑原始图像得到的模糊图像 b.用拉普拉斯算子增强上述a得到的模糊图像 c.用拉普拉斯算子增强原始图像 (For the original image <f5.tif>, to achieve the following results. a. smoothing filter with an average original image was blurred b. using Laplacian enhancement get a blurred image above c. using Laplacian enhancement of the original image)

基于多帧图像插值（Interpolation）技术的方法是SR恢复技术当中最直观 的方法。这类方法首先估计各帧图像之间的相对运动信息，获得HR图像在非均 匀间距采样点上的象素值，接着通过非均匀插值得到HR栅格上的象素值，最后 采用图像恢复技术来去除模糊和降低噪声（运动估计!非均匀插值!去模糊和 噪声）。(In this paper, we propose a novel method for solv- ing single-image super-resolution problems. Given a low-resolution image as input, we recover its high- resolution counterpart using a set of training exam- ples. While this formulation resembles other learning- based methods for super-resolution, our method has been inspired by recent manifold learning methods, par- ticularly locally linear embedding (LLE). Speci?cally, small image patches in the low- and high-resolution images form manifolds with similar local geometry in two distinct feature spaces. As in LLE, local geometry is characterized by how a feature vector correspond- ing to a patch can be reconstructed by its neighbors in the feature space. Besides using the training image pairs to estimate the high-resolution embedding, we also enforce local compatibility and smoothness con- straints between patches in the target high-resolution image through overlapping. Experiments show that our method is very ?exible )