Low-rank matrix recovery with poison noise
Publication
, Journal Article
Xie, Y; Chi, Y; Calderbank, R
Published in: 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
December 1, 2013
Estimating an image M* +m1×m2 from its linear measurements under Poisson noise is an important problem arises from applications such as optical imaging, nuclear medicine and x-ray imaging [1]. When the image M* has a low-rank structure, we can use a small number of linear measurements to recover M*, also known as low-rank matrix recovery. This is related to compressed sensing, where the goal is to develop efficient data acquisition systems by exploiting sparsity of underlying signals. © 2013 IEEE.
Duke Scholars
Published In
2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
DOI
Publication Date
December 1, 2013
Start / End Page
622
Citation
APA
Chicago
ICMJE
MLA
NLM
Xie, Y., Chi, Y., & Calderbank, R. (2013). Low-rank matrix recovery with poison noise. 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings, 622. https://doi.org/10.1109/GlobalSIP.2013.6736959
Xie, Y., Y. Chi, and R. Calderbank. “Low-rank matrix recovery with poison noise.” 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings, December 1, 2013, 622. https://doi.org/10.1109/GlobalSIP.2013.6736959.
Xie Y, Chi Y, Calderbank R. Low-rank matrix recovery with poison noise. 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings. 2013 Dec 1;622.
Xie, Y., et al. “Low-rank matrix recovery with poison noise.” 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings, Dec. 2013, p. 622. Scopus, doi:10.1109/GlobalSIP.2013.6736959.
Xie Y, Chi Y, Calderbank R. Low-rank matrix recovery with poison noise. 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings. 2013 Dec 1;622.
Published In
2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
DOI
Publication Date
December 1, 2013
Start / End Page
622