Semi-blind compressed sensing via adaptive dictionary learning and one-pass online extension  

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作  者:Di MA Songcan CHEN 

机构地区:[1]College of Computer Science and Technology,MIIT Key Laboratory of Pattern Analysis and Machine Intelligence,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China

出  处:《Science China(Information Sciences)》2021年第9期231-233,共3页中国科学(信息科学)(英文版)

基  金:This work was supported by Key Program of National Natural Science Foundation of China(Grant No.61732006)。

摘  要:Dear editor,Compressed sensing(CS) [1], as an efficient data acquisition paradigm, has attracted much attentions since it came up. The fundamental principle of CS is that a signal, which is sparse under some sparsity basis, can be efficiently acquired and accurately recovered via far fewer measurements than the traditional Shannon-Nyquist sampling.

关 键 词:EXTENSION EDITOR 

分 类 号:TN911.7[电子电信—通信与信息系统]

 

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