计算机工程与应用2019，Vol.55Issue(17)：1-8,19,9.DOI:10.3778/j.issn.1002-8331.1903-0437
深度学习框架下压缩感知重建算法综述
Survey of Compressed Sensing Reconstruction Algorithms in Deep Learning Framework
摘要
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.关键词
压缩感知/重建算法/深度学习/数据驱动Key words
compressed sensing/reconstruction algorithms/deep learning/data driven分类
信息技术与安全科学引用本文复制引用
曾春艳,叶佳翔,王志锋,武明虎..深度学习框架下压缩感知重建算法综述[J].计算机工程与应用,2019,55(17):1-8,19,9.基金项目
国家自然科学基金(No.61501199) （No.61501199）
湖北省自然科学基金(No.2017CFB683) （No.2017CFB683）
华中师范大学中央高校基本科研业务费项目(No.CCNU18QN021) （No.CCNU18QN021）
湖北省高等学校优秀中青年科技创新团队计划项目(No.T201805) （No.T201805）
太阳能高效利用及储能运行控制湖北省重点实验室开放研究基金(No.HBSEES201706). （No.HBSEES201706）
