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Comparison of synthesis-based and analysis-based compressive sensing

机译:基于合成和基于分析的压缩感测的比较

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The synthesis sparse representation model of signals regards that signal is formed from linear combination of a few atoms from a synthesis dictionary. Compressive sensing (CS) as a novel technique to acquire the signal directly in already compressed is based on that model. The analysis sparse representation as alternative model for the signals began to gain attention in recent years. The sparse analysis coefficients are obtained in analysis model by multiplying analysis dictionary and the signal. In this paper, we compare the performance of synthesis-based and analysis-based CS system. The simulation results show that analyisis-based CS provides better performance than synthesis-based CS in terms of signal recovery accuracy. It suggests that the analyis model will play an important role in the future direction of the CS research.
机译:信号的合成稀疏表示模型认为信号是由合成字典中几个原子的线性组合形成的。压缩感测(CS)是基于该模型的一种直接获取已压缩信号的新颖技术。近年来,分析稀疏表示作为信号的替代模型开始受到关注。通过将分析字典与信号相乘,可以在分析模型中获得稀疏的分析系数。在本文中,我们比较了基于综合和基于分析的CS系统的性能。仿真结果表明,基于信号分析的CS在信号恢复精度方面比基于合成的CS具有更好的性能。这表明分析模型将在CS研究的未来方向上发挥重要作用。

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