A FAST ALGORITHM FOR STRUCTURED LOW-RANK MATRIX RECOVERY WITH APPLICATIONS TO UNDERSAMPLED MRI RECONSTRUCTION.

Structured low-rank matrix priors are emerging as powerful alternatives to traditional image recovery methods such as total variation (TV) and wavelet regularization. The main challenge in applying these schemes to large-scale problems is the computational complexity and memory demand resulting from a lifting of the image to a high-dimensional dense matrix. We introduce a fast and memory efficient algorithm that exploits the convolutional structure of the lifted matrix to work in the original non-lifted domain, thus considerably reducing the complexity. Our experiments on the recovery of MR images from undersampled measurements show that the resulting algorithm provides improved reconstructions over TV regularization with comparable computation time.