MétaCan
Menu
Back to cohort
Record W3118378101 · doi:10.1364/josab.405022

Super-resolution far-field sub-wavelength imaging using multiple holography

2021· article· en· W3118378101 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the Optical Society of America B · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDigital Holography and Microscopy
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHolographyDiffractionWavelengthOpticsResolution (logic)Limit (mathematics)Iterative reconstructionImage resolutionNear and far fieldComputer scienceObject (grammar)Process (computing)PhysicsField (mathematics)Computer visionArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Losing the information contained in evanescent waves scattered from an object limits the best achievable resolution in far-field optical imaging systems to about half of the wavelength. This limitation is known as the diffraction limit. In this paper, we propose a new holography-based far-field imaging technique to go beyond the diffraction limit and achieve super-resolution images. In the proposed method, after the recording process, multiple reconstruction processes with appropriate reconstruction waves are performed to extract information about sub-wavelength features of a target object encoded in the evanescent waves scattered from it. It is analytically proved that in the proposed method, by increasing the number of reconstruction steps, the resolution increases. The performance of the method is numerically validated. In numerical analysis, by performing two reconstruction steps, a resolution of 1/14 of the working wavelength is achieved. This resolution can be further improved by increasing the number of reconstruction steps.

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.

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 score0.310
Threshold uncertainty score0.316

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 communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.244
Teacher spread0.234 · 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