Image Compressive Sensing Recovery via Collaborative Sparsity

@article{Zhang2012ImageCS,
  title={Image Compressive Sensing Recovery via Collaborative Sparsity},
  author={Jian Zhang and Debin Zhao and Chen Zhao and Ruiqin Xiong and Siwei Ma and Wen Gao},
  journal={IEEE Journal on Emerging and Selected Topics in Circuits and Systems},
  year={2012},
  volume={2},
  pages={380-391}
}
Compressive sensing (CS) has drawn quite an amount of attention as a joint sampling and compression approach. Its theory shows that when the signal is sparse enough in some domain, it can be decoded from many fewer measurements than suggested by the Nyquist sampling theory. So one of the most challenging researches in CS is to seek a domain where a signal can exhibit a high degree of sparsity and hence be recovered faithfully. Most of the conventional CS recovery approaches, however, exploited… CONTINUE READING
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