Math @ Duke

Publications [#235764] of Robert Calderbank
Papers Published
 Xie, Y; Chi, Y; Calderbank, R, Lowrank matrix recovery with poison noise,
2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013  Proceedings
(December, 2013),
pp. 622 [doi]
(last updated on 2017/12/16)
Abstract: Estimating an image M* + m 1 ×m 2 from its linear measurements under Poisson noise is an important problem arises from applications such as optical imaging, nuclear medicine and xray imaging [1]. When the image M* has a lowrank structure, we can use a small number of linear measurements to recover M*, also known as lowrank 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.


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