repeatability_demo

压缩感知中利用增广拉格朗日方程解最小稀疏正则化的恢复算法(DAL solves the dual problem of (1) via the augmented Lagrangian method (see Bertsekas 82). It uses the analytic expression (and its derivatives) of the following soft-thresholding operation， which can be computed for L1 and grouped L1 (and many other) sparsity inducing regularizers. If you are interested in our algorithm please find more details in our technical report or in my talk at Optimization for Machine Learning Workshop (NIPS 2009).)

用点扩散函数psf对波前编码系统产生的模糊图像进行复原得到清晰图像(use the PSF of wavefront coding system to restore its blurred image )

基于相似度的图像质量评价算法Structural SIMilarity (SSIM) index的实现。(This is an implementation of the algorithm for calculating the Structural SIMilarity (SSIM) index between two images. Please refer to the following paper: Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity" IEEE Transactios on Image Processing, vol. 13, no. 4, pp.600-612, )

一个基于帧间差分的目标跟踪matlab源代码，解压后可直接运行，对学习跟踪有重大帮助(Frame difference of a target tracking based on matlab source code, extract can be directly run a major help in learning track)

％通过耦合神经网络结合中值滤波实现图像的降噪源代码( Through the combination of coupled neural network filtering image noise source code)

说明：  中值滤波 去燥MATLAB代码 可直接用 可直接用 可直接用(Wavelet de-drying MATLAB code Can be used directly Can be used directly Can be used directly)

文件包含脑部CT图像分割的程序及所用图片(File contains the brain CT image segmentation procedures and images used in)

经典的ukf跟踪框架与源码，有详细的注释以及说明，适合初学者。(Classic ukf tracking framework and source code, detailed notes and instructions for beginners.)

说明：  计算机图形学中应用VC++6.0和DirectX 8.0 SDK制作彩色旋转的金字塔和正方体的小程序(computer graphics application VC 6.0 and DirectX 8.0 SDK produced color rotating cube pyramid and the small program)

图片去噪：对一幅图像加入不同的噪声（随机点噪声、椒盐噪声等），选取不同的方法去噪，比如说邻域平均、中值滤波、图像迭加等，比较对于不同的噪声，不同的方法哪种更好(Image denoising: for an image by adding different noise (random-dot noise, salt and pepper noise, etc.), select a different method of denoising, for example, the neighborhood average, median filter, image superposition and so on, compared for different noise, different methods which better)