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Record W2152161227 · doi:10.1109/ccece.2009.5090241

Improved hybrid demosaicing and color super-resolution implementation using quasi-Newton algorithms

2009· article· en· W2152161227 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Image Processing Techniques
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceDemosaicingMinificationAlgorithmGradient descentResolution (logic)Image (mathematics)Image resolutionLow resolutionSet (abstract data type)Image qualityFunction (biology)Artificial intelligenceSuperresolutionMethod of steepest descentComputer visionHigh resolutionColor imageMathematicsImage processingMathematical optimizationArtificial neural network

Abstract

fetched live from OpenAlex

Super-resolution algorithms can be used to reconstruct a high-resolution high-quality image from a set of low-quality images. A novel hybrid demosaicing and color super-resolution approach proposed by Farsiu, Elad, and Milanfar relies on the minimization of a nonconvex multiterm objective function using a rudimentary fixed step-size steepest-descent approach. In this paper, we show that improved performance can be achieved by implementing this approach in terms of powerful quasi-Newton algorithms.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.842
Threshold uncertainty score0.553

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 communication0.0000.002
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 head0.021
GPT teacher head0.328
Teacher spread0.307 · 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

Quick stats

Citations5
Published2009
Admission routes1
Has abstractyes

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