Learning sparse representations for adaptive compressive sensing


Breakthrough results in compressive sensing (CS) have shown that high dimensional signals (vectors) can often be accurately recovered from a relatively small number of non-adaptive linear projection observations, provided that they possess a sparse representation in some basis. Subsequent efforts have established that the reconstruction performance of CS… (More)
DOI: 10.1109/ICASSP.2012.6288324


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@article{Soni2012LearningSR, title={Learning sparse representations for adaptive compressive sensing}, author={Akshay Soni and Jarvis D. Haupt}, journal={2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year={2012}, pages={2097-2100} }