Compressed Sensing: Theory and Applications
Compressed Sensing: Theory and Applications
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DOI：
10.1088/1742-6596/2419/1/012042
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Hanbo Wang
中科院分区：
文献类型：
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作者：
Hanbo Wang
压缩感知是一种求解欠定线性系统的新技术。由于其良好的性能，在学术界得到了广泛的应用。它在电气工程中应用于稀疏信号的恢复，特别是在信号处理中。这项技术利用了信号的稀疏性，允许从较少的样本恢复原始信号。本文论述了压缩感知理论的基本原理，压缩感知信号处理的研究进展，以及压缩感知理论在核磁共振成像和地震勘探采集中的应用。压缩传感允许使用廉价的传感器对模拟数据进行数字化，并降低相关的处理、存储和传输成本。在其复杂的数学表达式背后，压缩感知理论蕴含着一种微妙的思想。压缩感知是一种新的理论，是对传统信号处理的理想补充和改进。它是一个具有很高生命力的理论，其研究成果可能会对信号处理等领域产生重大影响。
Compressed sensing is a new technique for solving underdetermined linear systems. Because of its good performance, it has been widely used in academia. It is applied in electrical engineering to recover sparse signals, especially in signal processing. This technique exploits the signal’s sparse nature, allowing the original signals to recover from fewer samples. This paper discusses the fundamentals of compressed sensing theory, the research progress in compressed sensing signal processing, and the applications of compressed sensing theory in nuclear magnetic resonance imaging and seismic exploration acquisition. Compressed sensing allows for the digitization of analogue data with inexpensive sensors and lowers the associated costs of processing, storage, and transmission. Behind its sophisticated mathematical expression, compressed sensing theory contains a subtle idea. Compressed sensing is a novel theory that is an ideal complement and improvement to conventional signal processing. It is a theory with a high vitality level, and its research outcomes may substantially influence signal processing and other fields.