Sparse Recovery Algorithms for 3D Imaging using Point Spread Function EngineeringPP

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

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