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 compression sensing is to use as few linear measurements as possible to represent a joint sparse signal. Distributed compression sensing joint reconstruction algorithm is based on the signal of a signal as side information, according to the signal focus on the correlation between signals to reconstruct the signal algorithm. In order to solve the complexity of reconstructed algorithms and reduce the number of measurements required for reconstruction, two new joint reconstruction algorithms for distributed compressed sensing are proposed. The two new algorithms proposed in the signal and image processing experiments, verify its feasibility and advanced. The results show that these two joint reconstruction algorithms require less measurement to obtain the same image quality.