Although experiments have proved that the unrestricted 12$\\ell _{1-2}$‐minimization model can recover the original sparse signal, the theoretical proof has not been established yet. This paper mainly proves theoretically that the unrestricted 12$\\ell _{1-2}$‐minimization ...
The nuclear idea is to downsample the input signal at the beginning;then, subsequent processing operates under downsampled signals, where signallengths are proportional to O(K). Downsampling, however, possibly leads to"aliasing." By the shift property of DFT, we recast the aliasing problem as...
Fast Fourier Transform (FFT) is one of the most important tools in digitalsignal processing. FFT costs O(N \\log N) for transforming a signal of length N.Recently, Sparse Fourier Transform (SFT) has emerged as a critical issueaddressing how to compute a compressed Fourier transform of a si...
The nuclear idea is to downsample the input signal at the beginning; then, subsequent processing operates under downsampled signals, where signal lengths are proportional to O(K). Downsampling, however, possibly leads to "aliasing." By the shift property of DFT, we recast the aliasing problem ...
For any k sparse signal 翻译结果2复制译文编辑译文朗读译文返回顶部 for any of the sparse signal K; 翻译结果3复制译文编辑译文朗读译文返回顶部 For any k-sparse signals 翻译结果4复制译文编辑译文朗读译文返回顶部 For any of the sparse signal K ...
Consider an N dimensional signal space RN, a K-sparse 2 signal x in this space would be represented as a K-dimension 选择语言:从 到 翻译结果1翻译结果2 翻译结果3翻译结果4翻译结果5 翻译结果1复制译文编辑译文朗读译文返回顶部 正在翻译,请等待... 翻译结果2复制译文编辑译文朗读译文返回顶部 翻译...
Sparse component analysis (SCA) is a popular method for addressing underdetermined blind source separation in array signal processing applications. We are motivated by problems that arise in the applications where the sources are densely sparse (i.e. the number of active sources is high and very ...
Magnetic Resonance Imaging (MRI) super-resolution image reconstruction algorithm, is presented in the paper. It is shown that the approach improves MRI spatial resolution in cases Compressed Sensing (CS) sequences are used. Compressed sensing (CS) aims at signal and images reconstructing from signifi...
The K-SVD algorithm is a highly efiective method of training overcomplete dic- tionaries for sparse signal representation. In this report we discuss an e-cient im- plementation of this algorithm, which both accelerates it and reduces its... R Rubinstein,M Zibulevsky,M Elad 被引量: 792发...
Employing the K-means algorithm in the suggested manner has the capability to improve model performance while mitigating the risk of overfitting. Also, it is more efficient compared to using signal decomposition techniques, since it reduces the number of executed models for each prediction to one. ...