REVIEW 1 cited by
Adaptive Super-Resolution Imaging Without Prior Knowledge Using a Programmable Spatial-Mode Sorter
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
We consider an imaging system tasked with estimating the angular distance between two incoherently-emitting, identically bright, sub-Rayleigh-separated point sources, without any prior knowledge of the centroid or the constellation and with a fixed collected-photon budget. It was shown theoretically that splitting the optical recording time into two stages -- focal-plane direct imaging to obtain a pre-estimate of the centroid, and using that estimate to center a spatial-mode sorter followed by photon detection of the sorted modes -- can achieve lower mean squared error in estimating the separation~\cite{Grace:20}. In this paper, we demonstrate this in a proof-of-concept, using a programmable mode sorter we have built using multi-plane light conversion (MPLC) using a reflective spatial-light modulator (SLM) in an emulated experiment where we use a single coherent source to characterize the MPLC to electronically piece together the signature from two closely-separated quasi-monochromatic incoherent emitters. We show an improvement in estimator variance when compared to direct imaging, in good agreement with simulations.
Forward citations
Cited by 1 Pith paper
-
Quantum-limited imaging using diffractive optical neural networks
A trained diffractive optical neural network with photon counting is shown in simulation to saturate the Nagaoka-Hayashi quantum bound for multiparameter imaging, outperforming direct imaging.
Discussion (0). Continue with ORCID to comment.