title: Structured illumination microscopy with unknown patterns and a statistical prior.
abstract: Structured illumination microscopy (SIM) improves resolution by down-modulating high-frequency information of an object to fit within the passband of the optical system. Generally, the reconstruction process requires prior knowledge of the illumination patterns, which implies a well-calibrated and aberration-free system. Here, we propose a newalgorithmic self-calibrationstrategy for SIM that does not need to know the exact patternsa priori, but only their covariance. The algorithm, termed PE-SIMS, includes a pattern-estimation (PE) step requiring the uniformity of the sum of the illumination patterns and a SIM reconstruction procedure using a statistical prior (SIMS). Additionally, we perform a pixel reassignment process (SIMS-PR) to enhance the reconstruction quality. We achieve 2× better resolution than a conventional widefield microscope, while remaining insensitive to aberration-induced pattern distortion and robust against parameter tuning.
authors.affiliation: Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, CA 94720, USA.
authors.forename: Li-Hao
authors.initials: LH
authors.lastname: Yeh
authors.affiliation: Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, CA 94720, USA; Electrical & Computer Engineering, Boston University, Boston, MA 02215, USA.
authors.forename: Lei
authors.initials: L
authors.lastname: Tian
authors.affiliation: Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, CA 94720, USA.
authors.forename: Laura
authors.initials: L
authors.lastname: Waller
keywords: (100.1455) Blind deconvolution; (100.6640) Superresolution; (110.0180) Microscopy; (110.1758) Computational imaging