搜索资源列表
variational_PDE_tools
- 变分偏微分方程图像处理工具包，包括计算曲率，方差，L1,L2范数等29个实用算法-variational PDE image processing tool kit, including the calculation of curvature, variance, L1, L2 norm, and other practical algorithms 29
ReadRINEX
- GPS中一个读取RINEX程序模块，可由给定的卫星号及历元时刻读出C1，P1，P2，L1和L2观测量.-GPS RINEX a reading program modules can be given to the satellite, and Epoch moment to read out C1, P1, P2, L1 and L2 observables.
DSP_JPEG2000_001.pdf
- 本文介绍了JPEG2000的静态图像压缩技术在TI TMS320DM642 DSP上的实现过程；对压缩技术核心小波算法结构进行改进，对硬件特点的程序结构、具体代码及总线读写等方面的改进作了详细描述．改进后的软件系统，对于1 024×768像素大小的图像、数据存储空间仅需6 KB，使得在资源有限的DSP芯片上能够运行JPEG2000图像压缩技术；编码速度相对于未改进的提高了l2～ 2O倍，可迭到3帧／s；压缩比为O．5 bit／p时，峰值信噪比可达到3O．-This paper introduce
ROFL2denoisingmodel
- ROF-L2去噪模型，可以有效的去除加性噪声-ROF-L2 denoising model
l1magic
- This package contains code for solving seven optimization problems. -The main directory contains MATLAB m-files which contain simple examples for each of the recovery problems. They illustrate how the code should be used (it is fairly straightfor
CalISI
- Function Definition - C: equalizer of MISO with N.L1 X 1 F: convolved mixing matrix with N X M, with length L2 of mixing polynomial matrix, denoted by N.L2 X M M: the number of inputs N: the observations of system L1: le
L2-1_modeling
- 线性系统理论的参考课件，对于学习者更好的掌握这门课程有很大的帮助-Linear system theory of reference courseware for the learner a better grasp of this course very helpful
cart_ktFOCUSS
- CS理论应用在磁共振成像上。本算法将FOCUSS应用到CS理论上并做了改进，有较好的重建效果。-FOCUSS is originally designed to reconstruct sparse signal. Furthermore, L1 optimization is achieved by successively solving weighted L2 optimization problem, which can be easily implemented. From these
irntv
- TV正则化去卷积the Iteratively Reweighted Norm algorithm for solving the generalized TV functional, which includes the L2-TV and and L1-TV problems-An Iteratively Reweighted Norm Algorithm for Minimization of Total Variation Functionals
error2d
- Computes the FE error measured in the L2- and H1-norm for 2D problems. Especially useful for illustrating the error decay of piecewise linear finite elements in the common norms.
FEMGUI
- This program solves the equation on the interval (0,1) plus boundary conditions of Dirichlet or Neumann type with the finite elemnt method. Linear and quadratic finite elemnst are included. In the case were an exact solution is given the L2-Error,
l2
- sparse recognition classification人脸识别。extend yaleB数据库10类-sparse recognition classification for face recognition. yaleB database
Wavelet-learning-materials
- 从数学的角度讲，小波是构造函数空间正交基的基本单元，是在能量有限空间L2(R) 上满足允许条件的函数，这样认识小波需要L2(R) 空间的基础知识，特别是内积空间中空间分解、函数变换等的基础知识。 从信号处理的角度讲，小波(变换)是强有力的时频分析(处理)工具，是在克服傅立叶变换缺点的基础上发展而来的，所以从信号处理的角度认识小波，需要傅立叶变换、傅立叶级数、滤波器等的基础知识。-From the mathematical point of view, is to construct a
l2lap
- 采用L2范数的超分辨率图像重建,和拉普拉斯结合用！-super resolution reconstruction algorithm
RSC
- 人脸识别的稀疏表示识别方法将稀疏表示的保真度表示为余项的L2范数，但最大似然估计理论证明这样的假设要求余项服从高斯分布，实际中这样的分布可能并不成立，特别是当测试图像中存在噪声、遮挡和伪装等异常像素，这就导致传统的保真度表达式所构造的稀疏表示模型对上述这些情况缺少足够的鲁棒性。而最大似然稀疏表示识别模型则基于最大似然估计理论，将保真度表达式改写为余项的最大似然分布函数，并将最大似然问题转化为一个加权优化问题-Recently the sparse representation (or codin
Sobel
- 本算法是在经典Sobel算子基础上,结合汽车本身的实际情况,增加了6个方向模板,同时利用基于L2 范数的各同性扩散去噪模型消除噪声,最终得出汽车轮廓图。仿真结果表明:该算法对汽车图像的噪声干扰 有较强的抑制能力,汽车轮廓提取定位更准,精度更高,图像更清晰。-This algorithm is the classical Sobel operator on the basis of the actual situation with the car itself, an increase
cpuinfo
- 这个函数读取各个比特的信息处理器和操作 系统，包括： *中央处理器的名字 *时钟速度 *处理器缓存的大小（二级） *号的物理处理器核心 *操作系统名称和版本 这是由程序/处理器（系统），sysctl（苹果）或命令行工具（窗口）。 需要在r2010a或以上。-This function reads various bits of information about the CPU and operating system, including: * CPU
L2
- 基于L2范数的多帧图像超分辨率图像重建算法，在现有算法基础上改进计算精度和效率。-Based on the L2 norm of the multi-frame image super-resolution image reconstruction algorithm, on the basis of existing algorithms to improve computational accuracy and efficiency.
HEM
- 采用自适应混合误差模型的正则化超分辨率算法，正则项采用BTV，参考文献：AN ADAPTIVE L1-L2 HYBRID ERROR MODEL TO SUPER-RESOLUTION -AN ADAPTIVE L1-L2 HYBRID ERROR MODEL TO SR
object-tracking-based-on-ML-and-L2
- 提出了一种基于ML和L2范数的视频目标跟踪算法。将压缩感知引入到目标跟踪领域，实现图像目标跟踪。-Presents a video object tracking algorithm based on ML and L2 norm. The compressed sensing is introduced into target tracking, image target tracking.