Dmitry Batenkov (Tel-Aviv University)

Abstract

The inverse problem of computational super-resolution is to recover fine features of a signal from bandlimited and noisy data. Despite long history of the question and its fundamental importance in science and engineering, relatively little is known regarding optimal accuracy of reconstructing the high resolution signal components, and how to attain it with tractable algorithms. In this talk I will describe recent progress on deriving optimal methods for super-resolving sparse sums of Dirac masses, a popular model in numerous applications such as spectral estimation, direction of arrival, imaging of point sources, and sampling signals below the Nyquist rate. Time permitting, I will also discuss generalizations of the theory and algorithms in several directions.