Title :
Multi-scale Bayesian reconstruction of compressive X-ray image
Author :
Jiaji Huang ; Xin Yuan ; Calderbank, Robert
Author_Institution :
Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA
Abstract :
A novel multi-scale dictionary based Bayesian reconstruction algorithm is proposed for compressive X-ray imaging, which encodes the material´s spectrum by Poisson measurements. Inspired by recently developed compressive X-ray imaging systems [1], this work aims to recover the material´s spectrum from the compressive coded image by leveraging a reference spectrum library. Instead of directly using the huge and redundant library as a dictionary, which is cumbersome in computation and difficult for selecting those active dictionary atoms, a multi-scale tree structured dictionary is refined from the spectrum library, and following this a Bayesian reconstruction algorithm is developed. Experimental results on real data demonstrate superior performance in comparison with traditional methods.
Keywords :
X-ray imaging; belief networks; compressed sensing; image reconstruction; trees (mathematics); Poisson measurements; active dictionary atoms; compressive X-ray imaging system; multiscale tree structured dictionary; novel multiscale dictionary based Bayesian reconstruction algorithm; reference spectrum library; Bayes methods; Dictionaries; Image reconstruction; Imaging; Libraries; Maximum likelihood estimation; X-ray imaging; Compressive sensing; X-ray imaging; dictionary; multi-scale; poisson;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on
Conference_Location :
South Brisbane, QLD
DOI :
10.1109/ICASSP.2015.7178244