2025
Texture and noise dual adaptation for infrared image super-resolution
Huang Y, Miyazaki T, Liu X, Dong Y, Omachi S. Texture and noise dual adaptation for infrared image super-resolution. Pattern Recognition 2025, 163: 111449. DOI: 10.1016/j.patcog.2025.111449.Peer-Reviewed Original ResearchTexture detailsAdversarial lossSuper-resolutionInfrared image super-resolutionVisible imagesImage super-resolutionState-of-the-artIR image qualityVisible light imagesAdversarial trainingExtraction branchUpsampling factorsBlurring artifactsImage processingModel adaptationAdaptive approachSpatial domainImage qualityNoiseInnovation frameworkLight imagesNoise transferDual adaptationImagesTexture distribution
2024
Heteroscedastic Uncertainty Estimation Framework for Unsupervised Registration
Zhang X, Pak D, Ahn S, Li X, You C, Staib L, Sinusas A, Wong A, Duncan J. Heteroscedastic Uncertainty Estimation Framework for Unsupervised Registration. Lecture Notes In Computer Science 2024, 15002: 651-661. DOI: 10.1007/978-3-031-72069-7_61.Peer-Reviewed Original ResearchUnsupervised registrationReal-world medical imagesCollaborative training strategyMedical image datasetsDeep learning methodsAccurate displacement estimationSignal-to-noise ratioImage datasetsRegistration architectureLearning methodsMedical imagesTraining strategyNoise distributionUncertainty estimationWeighting schemeRegistration performanceSpatial domainEstimation frameworkInput-dependentUncertainty estimation frameworkUniform noise levelsDisplacement estimationFrameworkNoise levelUnsupervised
2021
A Directionally Varying Change Points Model for Quantifying the Impact of a Point Source
Song J, Warren J. A Directionally Varying Change Points Model for Quantifying the Impact of a Point Source. Journal Of Agricultural, Biological And Environmental Statistics 2021, 27: 46-62. DOI: 10.1007/s13253-021-00466-y.Peer-Reviewed Original Research
2020
A Nonstationary Spatial Covariance Model for Processes Driven by Point Sources
Warren J. A Nonstationary Spatial Covariance Model for Processes Driven by Point Sources. Journal Of Agricultural, Biological And Environmental Statistics 2020, 25: 415-430. DOI: 10.1007/s13253-020-00404-4.Peer-Reviewed Original Research
2012
Bayesian spatial–temporal model for cardiac congenital anomalies and ambient air pollution risk assessment
Warren J, Fuentes M, Herring A, Langlois P. Bayesian spatial–temporal model for cardiac congenital anomalies and ambient air pollution risk assessment. Environmetrics 2012, 23: 673-684. PMID: 23482298, PMCID: PMC3589577, DOI: 10.1002/env.2174.Peer-Reviewed Original ResearchPrior distributionData settingMultivariate kernelSimulation study resultsMore standard modelsBayesian spatial-temporal modelsCompetitor modelsStandard modelSpatial domainModel outputSpatial-temporal modelSpaceMultivariate probit regression modelGreater flexibilityModelProbit regression modelKernelDistributionContinuous mannerTerms
2008
The Cerebellum Predicts the Timing of Perceptual Events
O'Reilly J, Mesulam M, Nobre A. The Cerebellum Predicts the Timing of Perceptual Events. Journal Of Neuroscience 2008, 28: 2252-2260. PMID: 18305258, PMCID: PMC6671847, DOI: 10.1523/jneurosci.2742-07.2008.Peer-Reviewed Original ResearchConceptsPerceptual predictionsPosterior cerebellumFunctional connectivityChange-over-timeFunctional magnetic resonance imagingOccluded moving objectSensory-motor networkAttentional orientingOrienting networkCerebellar regionsBrain metricsPerceptual judgmentsNeural systemsPerceptual eventsCortical networksCerebellumMagnetic resonance imagingHand movementsResonance imagingSpatial orientationTemporal-spatial modelObject motionPutamenSpatial domainPredicting changes
2003
Motion Analysis of 3D Ultrasound Texture Patterns
Yu W, Lin N, Yan P, Purushothaman K, Sinusas A, Thiele K, Duncan J. Motion Analysis of 3D Ultrasound Texture Patterns. Lecture Notes In Computer Science 2003, 2674: 253-261. DOI: 10.1007/3-540-44883-7_26.Peer-Reviewed Original Research
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