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Record W2094818091 · doi:10.1109/icip.2014.7026100

Multi-view video super-resolution for hybrid cameras using modified NLM and adaptive thresholding

2014· article· en· W2094818091 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 institutionsMcMaster University
Fundersnot available
KeywordsComputer scienceArtificial intelligenceThresholdingComputer visionPixelImage resolutionFrame (networking)Frame rateExploitDisplay resolutionFidelityImage (mathematics)

Abstract

fetched live from OpenAlex

Dual-mode (hybrid) cameras are able to simultaneously shoot two types of video streams: a high resolution with low frame rate stream and similarly a low resolution with high frame rate stream. There are some works on super-resolving the single camera video sequences. In this paper we propose a method for multi-view video super-resolution, utilizing sequences generated from a hybrid camera. In the proposed method we exploit the self-similarity in the spatial and temporal domains to reconstruct a pixel. We modify the nonlocal means method to be applied in our method. Through a combination of techniques including adaptive thresholds and specialized candidate pixel selection schemes, the proposed method reconstructs a high fidelity video stream with considerably improved performance.

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 score0.956
Threshold uncertainty score0.620

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 head0.063
GPT teacher head0.314
Teacher spread0.251 · 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
Published2014
Admission routes1
Has abstractyes

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