Generalized recovery algorithm for 3D super-resolution microscopy using rotating point spread functions.

ABSTRACT: Super-resolution microscopy with phase masks is a promising technique for 3D imaging and tracking. Due to the complexity of the resultant point spread functions, generalized recovery algorithms are still missing. We introduce a 3D super-resolution recovery algorithm that works for a variety of phase masks generating 3D point spread functions. A fast deconvolution process generates initial guesses, which are further refined by least squares fitting. Overfitting is suppressed using a machine learning determined threshold. Preliminary results on experimental data show that our algorithm can be used to super-localize 3D adsorption events within a porous polymer film and is useful for evaluating potential phase masks. Finally, we demonstrate that parallel computation on graphics processing unit

SUBMITTER: Shuang B 

PROVIDER: S-EPMC4973222 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature