Low-rank matrix recovery with poison noise

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Xie, Y; Chi, Y; Calderbank, R

Published Date

  • December 1, 2013

Published In

  • 2013 Ieee Global Conference on Signal and Information Processing, Globalsip 2013 Proceedings

Start / End Page

  • 622 -

Digital Object Identifier (DOI)

  • 10.1109/GlobalSIP.2013.6736959

Citation Source

  • Scopus