Comparison of l1-Minimization and Iteratively Reweighted least Squares-l p-Minimization for Image Reconstruction from Compressive Sensing

@article{Endra2010ComparisonOL,
  title={Comparison of l1-Minimization and Iteratively Reweighted least Squares-l p-Minimization for Image Reconstruction from Compressive Sensing},
  author={Oey Endra and Dadang Gunawan},
  journal={2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies},
  year={2010},
  pages={85-88}
}
Compressive sensing is the recent technique in data acquisition that allows to reconstruct signal form far fewer samples than conventional method i.e. Shannon-Nyquist theorem use. In this paper, we compare l1-minimization and Iteratively Reweighted least Squares (IRlS)-lp-minimization algorithm to reconstruct image from compressive measurement. Compressive measurement is done by using random Gaussian matrix to encode the image that the first be divided into number of blocks to reduce to the… CONTINUE READING