【摘 要】
：
Dear editor,rnCompressed sensing (CS)[1],as an efficient data acquisi-tion 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 ac-quired and
【机 构】
：
College of Computer Science and Technology,MIIT Key Laboratory of Pattern Analysis and Machine Intel
论文部分内容阅读
Dear editor,rnCompressed sensing (CS)[1],as an efficient data acquisi-tion 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 ac-quired and accurately recovered via far fewer measurements than the traditional Shannon-Nyquist sampling.Since the number of measurements is much smaller than the num-ber of signal coefficients,there potentially exist multiple solutions for the unknown signal,leading to so-called ill-posedness.To solve this problem,CS adopts a common mechanism that regularizes the recovery problem by con-straining the original signal to be sparse under some spar-sity basis[2].Thus,the prior knowledge about the sparsity basis is critical for CS recovery.The often-used sparsity bases include the discrete cosine transform (DCT) and dis-crete wavelet transform (DWT).Although CS achieves high efficiency under these sparsity bases,these sparsity bases are data-independent,thus not optimal for CS recovery at hand[3].
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