COLLABORATIVE COMPRESSIVE X-RAY IMAGE RECONSTRUCTION

被引:0
|
作者
Huang, Jiaji [1 ]
Yuan, Xin [1 ]
Calderbank, Robert [1 ]
机构
[1] Duke Univ, Dept Elect & Comp Engn, Durham, NC 27708 USA
关键词
Poisson inversion; sensing matrix; compressive sensing; X-ray imaging; Bayesian inference; NOISE;
D O I
暂无
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
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.
引用
收藏
页码:3282 / 3286
页数:5
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