分布式压缩感知联合重构算法（英文）

Joint reconstruction algorithm for distributed compressed sensing

分布式压缩感知是用尽可能少线性测量值来表示一个联合稀疏信号。分布式压缩感知联合重构算法是以信号集中的某个信号为边信息,根据信号集中信号之间的相关关系来重构信号的算法。为了解决已有重构算法的复杂性以及减少重构算法所需的测量值数,提出了两种新的分布式压缩感知联合重构算法。对提出的两种新算法在信号和图像处理上进行了实验,验证了其可行性与先进性。结果表示,这两种联合重建算法在获取相同的图像质量时需要测量值更少。

Distributed compressed sensing is concerned with representing an enseml：le of jointly sparse signals using as few linear measurements as possible. Joint reconstruction algorithm for distributed compressed perception was based on the idea of using one of the signals as side information, and then reconstruct other signals by the correlation between the side information and other signals. To resolve the complexity of reconstruction algorithms and reduce the measurements, two novel joint reconstruction algorithms for distributed compressed sensing based on joint sparse models were presented in this paper. Its application in signals and images processing was presented which are on the basis of demonstrating its feasibility. The result represent that the two novel joint reconstruction algorithms need fewer measurements for getting the same quality.