关键词: 压缩感知, 重建算法, 深度学习, 数据驱动

Abstract: Compressed Sensing（CS） technology is a milestone in the field of signal processing, which samples signals far less than Nyquist frequency, and reconstructs the original signals with high probability. In recent years, the advantages of deep learning technology in feature extraction and pattern classification provide new ideas for CS. Data-driven method is adopted in deep learning-based compressed sensing reconstruction algorithm, which reduces the reconstruction time by an order of magnitude, and the reconstruction accuracy is comparable or higher. This paper focuses on the deep learning-based compressed sensing reconstruction methods, considering the traditional reconstruction methods, and divides them into three categories：prior knowledge-based, pure data-driven, mixed prior knowledge-driven and data-driven. The characteristics of typical algorithms, network structure and key steps are analyzed. Finally, three kinds of algorithms are analyzed and summarized, and the research prospects of deep learning technology applied to compressed sensing are prospected.

Key words: compressed sensing, reconstruction algorithms, deep learning, data driven