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Record W2320813190 · doi:10.1541/ieejeiss.133.908

Sharpness-enhancing Enlargement of Terahertz Images

2013· article· en· W2320813190 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

VenueIEEJ Transactions on Electronics Information and Systems · 2013
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Image Processing Techniques
Canadian institutionsBruker (Canada)
Fundersnot available
KeywordsInterpolation (computer graphics)Discrete wavelet transformImage scalingArtificial intelligenceAlgorithmParametric statisticsWaveletNoise (video)Image (mathematics)Peak signal-to-noise ratioMathematicsSIGNAL (programming language)Computer visionInverseTerahertz radiationComputer scienceWavelet transformImage processingOpticsPhysicsStatisticsGeometry

Abstract

fetched live from OpenAlex

This paper presents non-parametric sharpness-enhancing enlargement technique for Terahertz images that combines spatial interpolation and discrete wavelet transform (DWT) reconstruction. The proposed algorithm finds the details from a DWT analysis of an interpolated version, then an inverse DWT of the details and the approximation rescaled from the input image produces an enlarged image. The computer simulation shows the proposed method shows higher peak signal-to-noise ratio and the sharpness measured by Tenengrade and sum-modulus-difference (SMD) than popular image enlargement 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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score0.422

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 communication0.0000.003
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 head0.007
GPT teacher head0.231
Teacher spread0.224 · 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