DocumentCode :
730506
Title :
Collaborative compressive X-ray image reconstruction
Author :
Jiaji Huang ; Xin Yuan ; Calderbank, Robert
Author_Institution :
Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA
fYear :
2015
fDate :
19-24 April 2015
Firstpage :
3282
Lastpage :
3286
Abstract :
The Poisson Factor Analysis (PFA) is applied to recover signals from a Poisson compressive sensing system. Motivated by the recently developed compressive X-ray imaging system, Coded Aperture Coherent Scatter Spectral Imaging (CACSSI) [1], we propose a new Bayesian reconstruction algorithm. The proposed Poisson-Gamma (PG) approach uses multiple measurements to refine our knowledge on both sensing matrix and background noise to overcome the uncertainties and inaccuracy of the hardware system. Therefore, a collaborative compressive X-ray image reconstruction algorithm is proposed under a Bayesian framework. Experimental results on real data show competitive performance in comparison with point estimation based methods.
Keywords :
Bayes methods; X-ray imaging; compressed sensing; computerised instrumentation; data compression; image coding; image reconstruction; stochastic processes; Bayesian reconstruction algorithm; CACSSI; PFA; PG approach; Poisson compressive sensing system; Poisson factor analysis; Poisson-Gamma approach; background noise; coded aperture coherent scatter spectral imaging; collaborative compressive X-ray image reconstruction; point estimation based method; signal recovery; Apertures; Detectors; Imaging; Mathematical model; Maximum likelihood estimation; X-ray imaging; Bayesian inference; Poisson inversion; X-ray imaging; compressive sensing; sensing matrix;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on
Conference_Location :
South Brisbane, QLD
Type :
conf
DOI :
10.1109/ICASSP.2015.7178578
Filename :
7178578
Link To Document :
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