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Record W2359085385 · doi:10.1117/12.2223378

Photon-sparse microscopy: Trans-wavelength ghost imaging

2016· article· en· W2359085385 on OpenAlexaff
Reuben S. Aspden, Nathan R. Gemmell, Peter A. Morris, D. S. Tasca, Lena Mertens, Michael G. Tanner, Robert A. Kirkwood, Alessandro Ruggeri, Alberto Tosi, Robert W. Boyd, Gerald S. Buller, Robert H. Hadfield, Miles J. Padgett

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRandom lasers and scattering media
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPhotonPhysicsOpticsGhost imagingWavelengthIterative reconstructionNoise (video)DetectorPixelMicroscopyComputer visionComputer scienceImage (mathematics)

Abstract

fetched live from OpenAlex

Ghost imaging systems use down-conversion sources that produce twin output beams of position-correlated photons to produce an image of an object using photons that did not interact with the object. One of these beams illuminates the object and is detected by a single pixel detector while the image information is recovered from the second, spatially correlated, beam. We utilize this technique to obtain images of objects probed with 1.5μm photons whilst developing the image using a highly efficient, low-noise, photon-counting camera detecting the correlated photons at 460nm. The efficient transfer of the image information from infrared illumination to visible detection wavelengths and the ability to count single-photons allows the acquisition of an image while illuminating the object with an optical power density of only 100 pJ cm<sup>-2</sup> s<sup>-1</sup>. We apply image reconstruction techniques based on compressive sensing to reconstruct our images from data sets containing far fewer photons than conventionally required. This wavelength-transforming ghost imaging technique has potential for the imaging of light-sensitive specimens or where covert operation is desired.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.220
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicRandom lasers and scattering mediaFrench-language works237,207