DocumentCode
1790555
Title
Innovative CS imaging methods in transformed domains
Author
Moriyama, Takumi ; Anselmi, Nicola ; Oliveri, G. ; Massa, A.
Author_Institution
Dept. of Electr. & Electron. Eng., Univ. of Nagasaki, Nagasaki, Japan
fYear
2014
fDate
16-19 Nov. 2014
Firstpage
1
Lastpage
3
Abstract
The solution of linear microwave imaging problems is considered in this work through innovative classes of Compressive Sensing (CS) methods. More in detail, the formulation of the inversion process in transformed domains with sparseness-regularized formulations is considered. Representative numerical examples illustrating the potentialities and limitations of the arising CS inversion approaches are reported.
Keywords
compressed sensing; image representation; microwave imaging; transforms; compressive sensing method; innovative CS imaging methods; inversion process formulation; linear microwave imaging problems; sparseness-regularized formulations; transformed domains; Antennas; Bayes methods; Compressed sensing; Image reconstruction; Microwave imaging; Wavelet domain;
fLanguage
English
Publisher
ieee
Conference_Titel
Antenna Measurements & Applications (CAMA), 2014 IEEE Conference on
Conference_Location
Antibes Juan-les-Pins
Type
conf
DOI
10.1109/CAMA.2014.7003312
Filename
7003312
Link To Document