【摘要】 现代社会信息量的激增带来了信号采样、传输和存储的巨大压力,而近年来出现的压缩感知理论(Compressed Sensing,CS)为解决该问题提供了契机。该理论指出:对于稀疏或可压缩的信号,能够以远低于奈奎斯特频率对其进行采样,并通过设计重构算法来精确的恢复该信号。介绍了压缩感知理论的基本框架并讨论了该理论关于信号压缩的采样过程;综述了压缩感知理论的重构算法,其中着重介绍了最优化算法和贪婪算法并比较了各种算法之间的优劣,最后探讨了压缩感知理论重构算法未来的研究重点。通过对压缩感知理论重构算法较为系统的介绍和比较,为压缩感知重构算法的改进和应用提供了理论依据。更多还原

【Abstract】 With the rapid demanding for information,the existing systems are very difficult to meet the challenges of high speed sampling,large volume data transmission and storage.Recently,a new sampling theory called compressive sensing(CS) provides a golden opportunity for solving this problem.CS theory asserts that a signal or image,unknown but supposed to be sparse or compressible in some basis,can be subjected to fewer measurements than traditional methods,and be accurately reconstructed.Firstly,a brief overview of the CS theory framework was given in this paper and the sampling process about signal compression was discussed.Next,the reconstruction algorithm of CS theory was reviewed.Especially,the basis pursuit algorithm and greedy algorithms were introduced and the difference between them was explored.In the end,possible implication in the areas of CS data reconstruction was briefly discussed.This paper provides theory basis for the improvement and application of compressed sensing reconstruction algorithm.更多还原