LOW-RANK MATRIX RECOVERY OF DYNAMIC EVENTS

Low-rank matrix recovery problems arise in a variety of scientific and engineering applications. For instance, blind deconvolution in signal processing and communication, phase retrieval in computational imaging, and recommendation systems in machine learning. In this paper, we present an algorithm for reconstructing a time-varying low-rank matrix from sequential measurements. In particular, we present an algorithm for estimating video frames from dynamic measurements by exploiting inter-frame motion. We discuss two applications of our proposed model and algorithm: (1) Autocalibration in dynamic MRI. (2) Phase retrieval of a video signal. We demonstrate the performance of our method on real and synthetic data.