A Fast Algorithm for Convolutional Structured Low-rank Matrix Recovery.

ABSTRACT: Fourier domain structured low-rank matrix priors are emerging as powerful alternatives to traditional image recovery methods such as total variation (TV) and wavelet regularization. These priors specify that a convolutional structured matrix, i.e., Toeplitz, Hankel, or their multi-level generalizations, built from Fourier data of the image should be low-rank. The main challenge in applying these schemes to large-scale problems is the computational complexity and memory demand resulting from a lifting the image data to a large scale matrix. We introduce a fast and memory efficient approach called the Generic Iterative Reweighted Annihilation Filter (GIRAF) algorithm that exploits the convolutional structure of the lifted matrix to work in the original un-lifted domain, thus considerably red

SUBMITTER: Ongie G 

PROVIDER: S-EPMC5999344 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature