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Orthogonal Matching Pursuit Algorithms based on Double Selection Strategy

机译：基于双选择策略的正交匹配追求算法

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摘要

The greedy algorithm is a promising signal reconstruction technique in compressed sensing theory. The generalized orthogonal matching pursuit (gOMP) algorithm is widely known for its high reconstruction probability in recovering sparse signals from compressed measurements. In this paper, we introduce two algorithms based on the gOMP to address the signal reconstruction issue. In these two approaches, the double selection strategy is exploited to automatically select a more suitable reconstruction method according to the change of the support set. Therefore, the proposed methods have greater flexibility in atom selection and also can remove the erroneous atoms in the support set to enhance the reconstruction accuracy when compared to the gOMP. Simulation results show that the presented algorithms have better recovery performance for both one-dimensional sparse signals and two-dimensional image signals.
机译：贪婪算法是压缩感测理论的有希望的信号重建技术。广泛的正交匹配追求（GOMP）算法在恢复来自压缩测量的稀疏信号时的高重建概率众所周知。在本文中，我们介绍了基于GOMP的两种算法来解决信号重建问题。在这两个方法中，利用双重选择策略以根据支持集的变化自动选择更合适的重建方法。因此，所提出的方法在原子选择方面具有更大的灵活性，并且还可以在与GOMP相比时去除支撑件中的错误原子以增强重建精度。仿真结果表明，所呈现的算法对于一维稀疏信号和二维图像信号具有更好的恢复性能。

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