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- 实现信号稀疏变换、观测矩阵设计、重构算法等一系列最新理论成果。-Achieve sparse signal transformation, observation matrix design, reconstruction algorithm and a series of recent theoretical results.
CS_OMP
- OMP重构算法，步骤清晰，输入传感矩阵，变换基矩阵和带重构的信号即可运行-OMP reconstruction algorithm, clear steps, input sensor matrix transformation matrix and base band signal can be reconstructed run
BP
- 一维信号BP重构算法，先生成稀疏度为K的稀疏矩阵，再加入高斯白噪声进行算法重构以及质量衡量。-BP signal reconstruction algorithm for one dimensional, Mr. into sparse matrix sparsity of K, then the Gauss white noise and measure the quality of reconstruction algorithm.
nfm
- 简单的非负矩阵分解算法，实现图片的重构，迭代次数越大，越接近原图-Simple non-negative matrix factorization algorithm, the reconstruction of the picture, the greater the number of iterations, the closer to the original image
demo_theory
- 观测矩阵已知的 基于tval3算法的压缩感知信号重构-known observation matrix remodeling
Compressive_sensing
- 傅立叶变换矩阵对信号进行稀疏表示，用高斯随即观测矩阵观测，重构方法为征缴匹配追踪算法、压缩感知入门程序，经典之作- U5085 u7An2F3 u53D3 u53A2 u7R09 u09R0 U9635 u89C2 u6D4B uFF0C u91CD u678 u6B1 u6CD5 u4E3A u5F81 u7F34 u5339 u914D u8E U5E8F uFF0C u7ECF u5178 u4E4B u4F5C
XZX
- 全局低秩显著性检测算法首先根据自然图像前景目标和背景亮度、颜色的差异性重构出图像前景显著目标；然后利用低秩分解对图像中的非显著性区域进行抑制。(The global low-rank saliency detection algorithm first reconstructs the image foreground salient targets based on the difference between the natural image foreground target and t