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Record W2023963773 · doi:10.1109/cvprw.2012.6239193

Gradient domain color restoration of clipped highlights

2012· article· en· W2023963773 on OpenAlexaff
Mushfiqur Rouf, Cheryl Lau, Wolfgang Heidrich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage Enhancement Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHueClipping (morphology)Artificial intelligenceColoredComputer visionColor spaceColor correctionComputer scienceLightnessBoundary (topology)Color balanceMathematicsColor imageImage (mathematics)Image processingMathematical analysisMaterials science

Abstract

fetched live from OpenAlex

Sensor clipping destroys the hue of colored highlight regions by misrepresenting the relative magnitude of the color channels. This becomes particularly noticeable in regions with brightly colored light sources or specularities. We present a simple yet effective gradient-space color restoration algorithm for recovering the hue in such image regions. First, we estimate a smooth distribution of the hue of the affected region from information at its boundary. We combine this hue estimate with gradient information from channels unaffected by clipping to restore clipped color channels.

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 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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.201

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 communication0.0000.001
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 head0.015
GPT teacher head0.259
Teacher spread0.243 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations9
Published2012
Admission routes1
Has abstractyes

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