搜索资源列表
ROMP
- 基于Matlab编写压缩感知重建算法Regularized Orthogonal Matching Pursuit.
ROMP
- Regularized OMP by Vershyn and Kneede-Regularized OMP by Vershyn and Kneedell
CS_recovery_algorithms_OMP_SP_IHT
- 基于Matlab编写压缩感知重建算法集，包括OMP，CoSaMP，IHT，IRLS，GBP，SP和ROMP.-Matlab codes for CS recovery algorithms, including OMP, CoSaMP, IHT, IRLS, GBP, SP and ROMP.
OMP
- OMP算法在压缩传感中的应用，这是一个m文件。-Orthogonal Matching Pursuit Algorithm for Compressive Sensing
romp
- Regularized Orthogonal Matching Pursuit是正交匹配追踪算法的优化，可以用于稀疏信号的恢复以及压缩感知等-Regularized Orthogonal Matching Pursuit
BayesianCompressiveSensing
- 压缩感知文献及相应的程序-Compressed sensing literature and the corresponding program
romp
- 压缩感知代码,在matlab编程环境中实现。代码结构清晰易懂。-code of compression perception
romp
- 一种压缩感知重构算法ROMP 需要已知稀疏度-A compressed sensing reconstruction algorithm ROMP needs known sparsity
ROMP
- 视频压缩感知系统的基追踪算法仿真，可以运行通-Video compress and sensing system that based on BP.
romp
- 实现压缩感知的快速重构算法！算法简单易于实现-Reconstruction from partial Fourier data
CS-recovery-based-on-bayes
- 基于贝叶斯假设的压缩感知重构算法，介绍了算法迭代流程，并与OMP ROMP STOMP算法做了比较-Bayesian Hypothesis Testing Based Recovery for Compressed Sensing
CS
- CS的一些重构算法包括OMP，somp,romp,SAMP，CoSaMp，GPSR等等其中还包括了小波变换，dct变换-compressive sensing
romp
- 正则化正交匹配追踪算法的函数，用matlab编写，可以求解压缩感知的信号重构问题-Regularization function orthogonal matching pursuit algorithm, using matlab prepared, can solve problems compressed sensing signal reconstruction
romp
- In sum capacity of an OFDM system is maximized by allocating each subchannel to the user with the highest channel gain over this channel and distributing power among subchannels in a water-filling manner.
CS
- 压缩感知重构算法的几个代码，包括MP,OMP,ROMP,STOMP,交流学习-Several code, compressed sensing reconstruction algorithm including MP, OMP, ROMP, STOMP, the exchange of learning
code
- romp matlab code ortho match pursuit
ROMP
- ROMP算法的子程序，压缩感知算法，正则化匹配追踪算法-routina of romp
ROMP
- 压缩感知中的ROMP算法，经过测试可以直接使用 -Compressed sensing the ROMP algorithm, tested can be used directly
CS_ROMP
- CS中ROMP算法的matlab程序，可用于其仿真
ROMP算法
- 压缩感知经典算法——ROMP，可实现信号压缩重构，针对不同稀疏度测试重构精度。