We consider the high-resolution imaging problem of 3D point source image recovery from 2D data using methods based on point spread function (PSF) design. Finding the locations of point sources is a large-scale sparse 3D inverse problem and we have studied solution algorithms based on sparse recovery in compressed sensing using non-convex optimization. Applications to high-resolution single molecule localization microscopy are described, and numerical tests are presented.

This is poster number 67 in Poster Session

Authors:

Chao Wang (The Chinese University of Hong Kong)

Raymond H. Chan (Department of Mathematics, The Chinese University of Hong Kong)

Robert Plemmons (Wake Forest University)

Mila Nikolova (CMLA - CNRS ENS Cachan, University Paris-Saclay)

Sudhakar Prasad (Department of Physics and Astronomy, The University of New Mexico)

Keywords:

3d localization, image reconstruction, nonlinear optimization, rotating point spread function (rpsf), sparse recovery