基于综合和基于分析的压缩感知的比较

Oey Endra, D. Gunawan
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引用次数: 1

摘要

信号的合成稀疏表示模型认为信号是由合成字典中的几个原子的线性组合而成的。压缩感知(CS)是一种基于该模型直接获取已压缩信号的新技术。分析稀疏表示作为信号的替代模型近年来开始受到关注。将分析字典与信号相乘,得到分析模型中的稀疏分析系数。本文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,基于分析的CS在信号恢复精度方面优于基于合成的CS。这表明该分析模型将在未来的CS研究方向中发挥重要作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of synthesis-based and analysis-based compressive sensing
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.
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