COMPUTER SCIENCE PRESENTATION
Chao Wang, Postdoctoral Researcher
Wake Forest University and UT—Dallas
Sparse Recovery Algorithms for 3D Imaging Using Point Spread Function Engineering
Abstract: We are concerned with the high-resolution imaging and localization problem of 3D point source image recovery from 2D data using methods based on point spread function (PSF) design. A new technique patented by S. Prasad for applying rotating point spread functions with a single lobe to obtain depth from defocus is considered. Applications include high-resolution single molecule localization microscopy, as well as localization of space debris using a space-based telescope. We develop and apply several recovery algorithms for this problem. Finding the locations and fluxes is a large-scale sparse 3D inverse problem. We have developed solution algorithms based on matching pursuit and non-convex optimization. In the matching pursuit case, we develop and apply a single best replacement (SBR) algorithm for our 3D localization problem. Acceleration techniques on searching processing and pre-computation are proposed. In the nonconvex optimization case, we consider two kinds of noise models. The continuous exact l 0 model (CEL0) with a least squares data-fitting term is applied for the Gaussian noise model, and a new nonconvex regularization method with data-fitting term based on Kullback-Leibler (KL) divergence is proposed for the Poisson noise model. In addition, we propose a new scheme of estimation of the source fluxes from the KL data-fitting term. Numerical experiments illustrate the efficiency and stability of the algorithms.
Tuesday, July 31, 2018 at 3:30 PM in Manchester Hall, Room 229