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Expensive scientific camera hardware is amongst the main cost factors inmodern, high-performance microscopes. Recent technological advantages have,however, yielded consumer-grade camera devices that can provide surprisinglygood performance. The camera sensors of smartphones in particular havebenefited of this development. Combined with computing power and due to theirubiquity, smartphones provide a fantastic opportunity for "imaging on abudget". Here we show that a consumer cellphone is capable even of opticalsuper-resolution imaging by (direct) Stochastic Optical ReconstructionMicroscopy (dSTORM), achieving optical resolution better than 80 nm. Inaddition to the use of standard reconstruction algorithms, we investigated anapproach by a trained image-to-image generative adversarial network (GAN). Thisnot only serves as a versatile technique to reconstruct video sequences underconditions where traditional algorithms provide sub-optimal localizationperformance, but also allows processing directly on the smartphone. We believethat "cellSTORM" paves the way for affordable super-resolution microscopysuitable for research and education, expanding access to cutting edge researchto a large community.

cellSTORM - Cost-effective Super-Resolution on a Cellphone using dSTORM