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Record W4411580635 · doi:10.1364/ao.562915

Near-infrared multi-spectral imaging with Bayer-filter color cameras: a single-exposure approach for soot and temperature diagnostics

2025· article· en· W4411580635 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueApplied Optics · 2025
Typearticle
Languageen
FieldEngineering
TopicRadiative Heat Transfer Studies
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsOpticsInfraredSootMaterials scienceSpectral imagingFilter (signal processing)Spatial filterHyperspectral imagingPhysicsComputer scienceArtificial intelligenceComputer visionCombustion

Abstract

fetched live from OpenAlex

The color camera's application in combustion diagnostics is limited by the lack of absolute calibration methods, particularly in the near-infrared range. This study introduces a calibration approach for color cameras with Bayer filter arrays that extracts multi-wavelength spectral radiance from a single raw RGB image. By leveraging a matched multi-bandpass filter and the spectral sensitivity of the channels, the method compensates for spectral overlaps and eliminates the need for multiple exposures. Demonstrated on an ethylene-air flame with spectral emission and light extinction measurements at 550, 660, and 850 nm, the results are in agreement with prior studies, showcasing the potential of color cameras for near-infrared imaging in high-accuracy combustion diagnostics.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.009
GPT teacher head0.205
Teacher spread0.196 · 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