2 research outputs found
Better than a lens -- Increasing the signal-to-noise ratio through pupil splitting
Lenses are designed to fulfill Fermats principle such that all light
interferes constructively in its focus, guaranteeing its maximum concentration.
It can be shown that imaging via an unmodified full pupil yields the maximum
transfer strength for all spatial frequencies transferable by the system.
Seemingly also the signal-to-noise ratio (SNR) is optimal. The achievable SNR
at a given photon budget is critical especially if that budget is strictly
limited as in the case of fluorescence microscopy. In this work we propose a
general method which achieves a better SNR for high spatial frequency
information of an optical imaging system, without the need to capture more
photons. This is achieved by splitting the pupil of an incoherent imaging
system such that two (or more) sub-images are simultaneously acquired and
computationally recombined. We compare the theoretical performance of split
pupil imaging to the non-split scenario and implement the splitting using a
tilted elliptical mirror placed at the back-focal-plane (BFP) of a fluorescence
widefield microscope
cellSTORM - Cost-effective Super-Resolution on a Cellphone using dSTORM
Expensive scientific camera hardware is amongst the main cost factors in
modern, high-performance microscopes. Recent technological advantages have,
however, yielded consumer-grade camera devices that can provide surprisingly
good performance. The camera sensors of smartphones in particular have
benefited of this development. Combined with computing power and due to their
ubiquity, smartphones provide a fantastic opportunity for "imaging on a
budget". Here we show that a consumer cellphone is capable even of optical
super-resolution imaging by (direct) Stochastic Optical Reconstruction
Microscopy (dSTORM), achieving optical resolution better than 80 nm. In
addition to the use of standard reconstruction algorithms, we investigated an
approach by a trained image-to-image generative adversarial network (GAN). This
not only serves as a versatile technique to reconstruct video sequences under
conditions where traditional algorithms provide sub-optimal localization
performance, but also allows processing directly on the smartphone. We believe
that "cellSTORM" paves the way for affordable super-resolution microscopy
suitable for research and education, expanding access to cutting edge research
to a large community