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Record W3020570303 · doi:10.1063/5.0002069

Non-local edge enhanced imaging with incoherent thermal light

2020· article· en· W3020570303 on OpenAlexaff
Hanquan Song, Yingwen Zhang, Yuhang Ren, Zhidan Yuan, Dayu Zhao, Zhiyuan Zheng, Lu Gao

Bibliographic record

VenueApplied Physics Letters · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRandom lasers and scattering media
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsOpticsSpiral (railway)PhysicsPhase (matter)Ghost imagingFilter (signal processing)Enhanced Data Rates for GSM EvolutionWavelengthMaterials scienceComputer scienceArtificial intelligenceComputer vision

Abstract

fetched live from OpenAlex

Spiral phase contrast imaging is an effective technique for the edge enhancement of a phase object. The spiral phase filter is the core component of the system and it provides sensitivity to the phase and amplitude gradients of the object. General spiral phase contrast imaging depends on the 4f imaging system in a single light beam with the coherent light source of visible or infrared wavelengths. Here, we constructed a non-local edge enhanced imaging system using an incoherent thermal light source. The detected object and the adopted spiral phase filter were non-locally placed in two separated light beams. The edge enhanced ghost image of the phase object can then be achieved through second-order intensity correlation measurements. The classical nature of the spatial degree of freedom of our proposed edge enhanced ghost imaging system was also demonstrated through the measurement of Bell-type inequality.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.184
Teacher spread0.179 · 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 source (direct Gemma or distilled Codex), 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

Citations24
Published2020
Admission routes1
Has abstractyes

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