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Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement: An overview from a signal processing perspective

IEEE Signal Processing Magazine(2022)10.1109/msp.2021.3119273Source: DataRank Database

Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement: An overview from a signal processing perspective is a research paper published in IEEE Signal Processing Magazine (2022). On theSindex it has a DataRank of 2.2. It has been cited 79 times, with 65 citing works in its 1-hop citation network.

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2.2DataRank · unranked

Scored on demand from live citation data

Open Access79 citations · 65 citing works in its network

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Linked data & code

Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement · arXiv

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (9 grants)Impact (FWCI 16.66)OA: greenTopics (26)Linked Datasets (1)DataCite Reuse (1)IDs (PubMed, PMC, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (2/2)

Has DOI
Indexed in repositories

Accessible (1/2)

Open Access

Interoperable (2/2)

DataCite relations
Linked datasets

Reusable (0/3)

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DataRank Breakdown

Base Score 30%Citation Network 70%

Base Score Contribution

0.657

From this paper's citation signal

Citation Network Contribution

1.5

From 50 citing papers with measurable signal

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Top 5 citers driving the network score

Ranked by each citer's contribution to N(p) — log1p(C_q) divided by its reference count — out of 65 citers.

Wearable Sensor Data for Classification and Analysis of Functional Fitness Exercises Using Unsupervised Deep Learning Methodologies
Security and Communication Networks202246 citations22 referencesContributes 0.175
Enhancing Continuous Chaos Communication Using Machine Learning in Resource-Limited Devices
20232 citations13 referencesContributes 0.085
Image clearness processing for image restoration based on generative adversarial networks
International Journal of Cognitive Computing in Engineering20257 citations25 referencesContributes 0.083
Trustworthy Bayesian deep learning framework for uncertainty quantification and confidence calibration: Application in machinery fault diagnosis
Reliability Engineering & System Safety202446 citations55 referencesContributes 0.070
Domain adversarial-based multi-source deep transfer network for cross-production-line time series forecasting
Applied Intelligence202316 citations41 referencesContributes 0.069

Authors (4)

Burhaneddin Yaman ORCID,Hyungjin Chung ORCID,Jong Chul Ye ORCID,Mehmet Akçakaya ORCID

Live enrichment

PubMed:36186087 PMC:PMC9523517 OpenAlex:https://openalex.org/W4213412343

National Institutes of Health

Grant: R01HL153146

National Institutes of Health

Grant: P41EB027061

National Institutes of Health

Grant: R21EB028369

National Science Foundation

Grant: CCF-1651825

National Research Foundation of Korea

Grant: NRF-2020R1A2B5B03001980

National Institutes of Health

Grant: 5R01HL153146-02

Rapid Comprehensive Cardiac MRI Exam for Diagnosis of Coronary Artery Disease

National Institutes of Health

Grant: 3P41EB027061-03S1

Technology to Realize the Full Potential of UHF MRI (Supplement)

National Science Foundation

Grant: 0300198

Reliability Assessment of the Serviceability Performance of Geotechnical Structures Using Neural Networks

National Institutes of Health

Grant: 1R21EB028369-01A1

Novel Quantitative MRI Techniques for the Assessment of Cardiac Fibrosis without Gadolinium Contrast

FWCI

16.66

Citation Percentile

1.0%

Citation Trend

2022

2023

2024

2025

2026

greenLicense: IEEE Copyright

repositoryhttps://arxiv.org/pdf/2105.08040

publisherhttps://ieeexplore.ieee.org/ielam/79/9721176/9721153-aam.pdf

publisherhttp://xplorestaging.ieee.org/ielx7/79/9721176/09721153.pdf?arnumber=9721153

repositoryhttp://arxiv.org/abs/2105.08040

Fields of Study

Cell Image Analysis TechniquesAdvanced Electron Microscopy Techniques and ApplicationsAdvanced Fluorescence Microscopy Techniques0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

Keywords

Artificial intelligenceComputer scienceUnsupervised learningDeconvolutionInverse problemDeep learningContext (archaeology)Perspective (graphical)InferenceIterative reconstructionMachine learningBiological imagingImage processingSignal processingPattern recognition (psychology)Image (mathematics)AlgorithmMathematicsOpticsDigital signal processingImage reconstruction

Sustainable Development Goals

SDG 0: Quality Education

Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement

10.48550/arxiv.2105.08040arXiv