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Record W4385802380 · doi:10.1364/oe.498763

Microlens array camera with variable apertures for single-shot high dynamic range (HDR) imaging

2023· article· en· W4385802380 on OpenAlexaff
Young-Gil Cha, Jiwoong Na, Hyun-Kyung Kim, Jae-Myeong Kwon, Seok-Haeng Huh, Seung-Un Jo, Chang-Hwan Kim, Min H. Kim, Ki-Hun Jeong

Bibliographic record

VenueOptics Express · 2023
Typearticle
Languageen
FieldEngineering
TopicImage Processing Techniques and Applications
Canadian institutionsNexen (Canada)Kootenay Association for Science & Technology
FundersDefense Acquisition Program AdministrationNational Research Foundation of Korea
KeywordsMicrolensOpticsMultiple exposureDynamic rangeHigh dynamic rangeHigh-dynamic-range imagingImage sensorFocal lengthCMOS sensorMaterials scienceComputer sciencePhysicsLens (geology)

Abstract

fetched live from OpenAlex

We report a microlens array camera with variable apertures (MACVA) for high dynamic range (HDR) imaging by using microlens arrays with various sizes of apertures. The MACVA comprises variable apertures, microlens arrays, gap spacers, and a CMOS image sensor. The microlenses with variable apertures capture low dynamic range (LDR) images with different f-stops under single-shot exposure. The reconstructed HDR images clearly exhibit expanded dynamic ranges surpassing LDR images as well as high resolution without motion artifacts, comparable to the maximum MTF50 value observed among the LDR images. This compact camera provides, what we believe to be, a new perspective for various machine vision or mobile devices applications.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.233
Teacher spread0.222 · 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

Citations13
Published2023
Admission routes1
Has abstractyes

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