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Record W3129379239 · doi:10.1364/ao.415974

Fast full-field modulation transfer function analysis for photographic lens quality assessment

2021· article· en· W3129379239 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

VenueApplied Optics · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced optical system design
Canadian institutionsCanadian Society of Intestinal Research
Fundersnot available
KeywordsOptical transfer functionOpticsLens (geology)Image qualityTransfer functionModulation (music)Spatial frequencyPoint spread functionQuality (philosophy)Materials scienceComputer sciencePhysics

Abstract

fetched live from OpenAlex

Full-field modulation transfer function (MTF) data based on the slanted-edge method can give useful insights on the performance of a photographic lens sample and its shortcomings. Decentering and other out-of-tolerance states are recognized easily. A process to derive accurate lens MTF from slanted-edge spatial frequency response measurements is presented, covering chart design and alignment, data capture by standard digital cameras, slanted-edge algorithm implementation requirements, sensor and chart MTF corrections, and also visualization of the results. It is demonstrated that the reliability of the measured MTF values is by far good enough to support automated quality assessment with a measurement accuracy of ≈0.02 MTF and repeatability of ≲0.005 up to 100 c/mm. The measured full-field MTF values provide an unambiguous numerical criterion for comparison with expectations based on lens design.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.890
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.030
GPT teacher head0.269
Teacher spread0.239 · 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