Structured illumination microscopy with unknown patterns and a statistical prior
This work addresses a calibration challenge in microscopy for researchers, offering an incremental improvement by reducing sensitivity to system aberrations.
The authors tackled the problem of structured illumination microscopy requiring prior knowledge of illumination patterns by proposing an algorithmic self-calibration strategy that uses only pattern covariance, achieving 2× better resolution than conventional widefield microscopy while being robust to aberrations and parameter tuning.
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 new \textit{algorithmic self-calibration} strategy for SIM that does not need to know the exact patterns {\it a 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$\times$ better resolution than a conventional widefield microscope, while remaining insensitive to aberration-induced pattern distortion and robust against parameter tuning.