# Structured illumination microscopy with unknown patterns and a statistical prior

arXiv: https://arxiv.org/abs/1611.00287 · HTML: https://mlsanity.com/paper/1611.00287/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2016.

## Built on by (most cited first, 2 of 2)

- [Dynamic Structured Illumination Microscopy with a Neural Space-time Model](https://mlsanity.com/paper/2206.01397/) (2022)
- [Untrained, physics-informed neural networks for structured illumination microscopy](https://mlsanity.com/paper/2207.07705/) (2022)

## Abstract (arXiv)

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 textitalgorithmic 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× better resolution than a conventional widefield microscope, while remaining insensitive to aberration-induced pattern distortion and robust against parameter tuning.