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Bilevel Methods for Image Reconstruction

Foundations and Trends® in Signal Processing(2022)10.1561/2000000111Source: DataRank Database

Bilevel Methods for Image Reconstruction is a research paper published in Foundations and Trends® in Signal Processing (2022). On theSindex it has a DataRank of 0. It has been cited 26 times.

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Open Access26 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (2 grants)OA: goldTopics (13)IDs (PubMed)

FAIR Checklist

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Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (2)

Jeffrey A. Fessler ORCID,Caroline Crockett ORCID

Live enrichment

PubMed:21097893

National Science Foundation

Grant: 1838179

BIGDATA: F: Random and Adaptive Projections for Scalable Optimization and Learning

National Institutes of Health

Grant: 1R01EB023618-01

Fast Functional MRI with Sparse Sampling and Model-Based Reconstruction

goldLicense: CC BY

publisherhttps://doi.org/10.1561/2000000111

repositoryhttps://arxiv.org/pdf/2109.09610

https://dx.doi.org/10.48550/arxiv.2109.09610

http://arxiv.org/abs/2109.09610

https://zbmath.org/7644939

Fields of Study

0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

Keywords

statistical learningmachine learningdecodinginverse problemsOptimization and Control (math.OC)sparse representationsLearning and adaptive systems in artificial intelligenceFOS: MathematicsImage processing (compression, reconstruction, etc.) in information and communication theoryComputing methodologies for image processingMathematics - Optimization and Control