@inproceedings{086a1ec33fb04da0ba09f15feb9c423a,
title = "Comparison of synthesis-based and analysis-based compressive sensing",
abstract = "The synthesis sparse representation model of signals regards that signal is formed from linear combination of a few atoms from a synthesis dictionary. Compressive sensing (CS) as a novel technique to acquire the signal directly in already compressed is based on that model. The analysis sparse representation as alternative model for the signals began to gain attention in recent years. The sparse analysis coefficients are obtained in analysis model by multiplying analysis dictionary and the signal. In this paper, we compare the performance of synthesis-based and analysis-based CS system. The simulation results show that analyisis-based CS provides better performance than synthesis-based CS in terms of signal recovery accuracy. It suggests that the analyis model will play an important role in the future direction of the CS research.",
keywords = "analysis model, compressive sensing, sparse representation, synthesis model",
author = "Oey Endra and Dadang Gunawan",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 14th International Conference on QiR (Quality in Research), QiR 2015 ; Conference date: 10-08-2015 Through 13-08-2015",
year = "2016",
month = jan,
day = "7",
doi = "10.1109/QiR.2015.7374920",
language = "English",
series = "14th International Conference on QiR (Quality in Research), QiR 2015 - In conjunction with 4th Asian Symposium on Material Processing, ASMP 2015 and International Conference in Saving Energy in Refrigeration and Air Conditioning, ICSERA 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "167--170",
booktitle = "14th International Conference on QiR (Quality in Research), QiR 2015 - In conjunction with 4th Asian Symposium on Material Processing, ASMP 2015 and International Conference in Saving Energy in Refrigeration and Air Conditioning, ICSERA 2015",
address = "United States",
}