A Generalized Structured Low-Rank Matrix Completion Algorithm for MR Image Recovery
Abstract
Recent theory of mapping an image into a structured low-rank Toeplitz or Hankel matrix has become an effective method to restore images. In this paper, we introduce a generalized structured low-rank algorithm to recover images from their undersampled Fourier coefficients using infimal convolution regularizations. The image is modeled as the superposition of a piecewise constant component and a piecewise linear component. The Fourier coefficients of each component satisfy an annihilation relation, which results in a structured Toeplitz matrix, respectively. We exploit the low-rank property of the matrices to formulate a combined regularized optimization problem. In order to solve the problem efficiently and to avoid the high memory demand resulting from the large-scale Toeplitz matrices, we introduce a fast and memory efficient algorithm based on the half-circulant approximation of the Toeplitz matrix. We demonstrate our algorithm in the context of single and multi-channel MR images recovery. Numerical experiments indicate that the proposed algorithm provides improved recovery performance over the state-of-the-art approaches.
Cite
@article{arxiv.1811.10778,
title = {A Generalized Structured Low-Rank Matrix Completion Algorithm for MR Image Recovery},
author = {Yue Hu and Xiaohan Liu and Mathews Jacob},
journal= {arXiv preprint arXiv:1811.10778},
year = {2018}
}