TY - GEN
T1 - Analysis of projection optimization in compressive sensing framework into reconstruction performance
AU - Andryani, Nur Afny C.
AU - Sudiana, Dodi
AU - Gunawan, Dadang
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/23
Y1 - 2017/2/23
N2 - Compressive Sensing (CS), which is firmly mathematically formulated by Danoho D, Candes E, Romberg J, and Tao T, is much developed especially for sensing and signal reconstruction. Its advantage framework on reducing number of measurement data while maintaining the performance of reconstruction quality, makes many researchers concern on developing the compressive sensing performance. The main parameters in CS are projection matrix and sparse base representation (dictionary). Subject to Restricted Isometric Property, the more incoherence between projection matrix and the dictionary, the more precise the signal reconstruction. Thus, a number of fundamental researches regarding projection optimization to optimize the incoherence between projection matrix and the dictionary have been developed. This paper elaborate the analysis of projection optimization's impact into reconstruction performance on signal with random and structured projection matrix. The simulations show that the projection optimization does not always imply better reconstruction especially for signal reconstruction with structured projection matrix.
AB - Compressive Sensing (CS), which is firmly mathematically formulated by Danoho D, Candes E, Romberg J, and Tao T, is much developed especially for sensing and signal reconstruction. Its advantage framework on reducing number of measurement data while maintaining the performance of reconstruction quality, makes many researchers concern on developing the compressive sensing performance. The main parameters in CS are projection matrix and sparse base representation (dictionary). Subject to Restricted Isometric Property, the more incoherence between projection matrix and the dictionary, the more precise the signal reconstruction. Thus, a number of fundamental researches regarding projection optimization to optimize the incoherence between projection matrix and the dictionary have been developed. This paper elaborate the analysis of projection optimization's impact into reconstruction performance on signal with random and structured projection matrix. The simulations show that the projection optimization does not always imply better reconstruction especially for signal reconstruction with structured projection matrix.
KW - Compressive Sensing
KW - Projection Optimization
UR - http://www.scopus.com/inward/record.url?scp=85016056409&partnerID=8YFLogxK
U2 - 10.1109/IC3INA.2016.7863035
DO - 10.1109/IC3INA.2016.7863035
M3 - Conference contribution
AN - SCOPUS:85016056409
T3 - Proceeding - 2016 International Conference on Computer, Control, Informatics and its Applications: Recent Progress in Computer, Control, and Informatics for Data Science, IC3INA 2016
SP - 119
EP - 124
BT - Proceeding - 2016 International Conference on Computer, Control, Informatics and its Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 International Conference on Computer, Control, Informatics and its Applications, IC3INA 2016
Y2 - 3 October 2016 through 5 October 2016
ER -