.png){kind=icon}
Return
Convolutional Compressed Sensing Using Decimated Sidelnikov Sequences
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2014.2311659.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In many applications of compressed sensing, the data acquisition involves convolution by a filter followed by subsampling. In this letter, we propose to construct a filter with real-valued coefficients by taking the discrete Fourier transform of a decimated binary Sidelnikov sequence. With a random subsampler, we prove that stable recovery can be guaranteed if a signal is sparse in the canonical or the FFT basis. Besides, simulation results also show that if a deterministic subsampler is used, the proposed system can offer similar reconstruction performance as that of a random Gaussian operator for a wide range of signal length.
Keywords:
Coherence
convolutional compressed sensing
restricted isometry property
Sidelnikov sequences
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W
Organization
Cited Papers
.png){kind=icon}
ShareSaveResonance Raman study on yeast cytochrome c peroxidase Effect of coordination and axial ligands
PREAI
ShareSaveShareSaveShareSaveShareSave
View more