Sparse deconvolution of high-density super-resolution images.

ABSTRACT: In wide-field super-resolution microscopy, investigating the nanoscale structure of cellular processes, and resolving fast dynamics and morphological changes in cells requires algorithms capable of working with a high-density of emissive fluorophores. Current deconvolution algorithms estimate fluorophore density by using representations of the signal that promote sparsity of the super-resolution images via an L1-norm penalty. This penalty imposes a restriction on the sum of absolute values of the estimates of emitter brightness. By implementing an L0-norm penalty--on the number of fluorophores rather than on their overall brightness--we present a penalized regression approach that can work at high-density and allows fast super-resolution imaging. We validated our approach on simulated imag

SUBMITTER: Hugelier S 

PROVIDER: S-EPMC4766479 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature