@inproceedings{b92d2ce7ba1c47a7af536f0f629f3926,
title = "Comparison of ℓ1-minimization and iteratively reweighted least squares-ℓp-minimization for image reconstruction from compressive sensing",
abstract = "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 computational complexity. From the results, IRLS-ℓp and ℓ1-minimization provided almost the same image reconstruction quality, but the IRLS-ℓp-minimization resulted the faster computation than ℓ1-minimization algorithm.",
keywords = "Compressive sensing, Iteratively reweighted least squares-ℓ- minimization,ℓ-minimization",
author = "Oey Endra and Dadang Gunawan",
year = "2010",
doi = "10.1109/ACT.2010.31",
language = "English",
isbn = "9780769542690",
series = "Proceedings - 2010 2nd International Conference on Advances in Computing, Control and Telecommunication Technologies, ACT 2010",
pages = "85--88",
booktitle = "Proceedings - 2010 2nd International Conference on Advances in Computing, Control and Telecommunication Technologies, ACT 2010",
note = "2010 2nd International Conference on Advances in Computing, Control and Telecommunication Technologies, ACT 2010 ; Conference date: 02-12-2010 Through 03-12-2010",
}