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 distributionSuper‐resolution imaging of proteins inside live mammalian cells with mLIVE‐PAINT
Bhaskar H, Gidden Z, Virdi G, Kleinjan D, Rosser S, Gandhi S, Regan L, Horrocks M. Super‐resolution imaging of proteins inside live mammalian cells with mLIVE‐PAINT. Protein Science 2025, 34: e70008. PMID: 39865341, PMCID: PMC11761688, DOI: 10.1002/pro.70008.Peer-Reviewed Original Research
2024
A Flow-based Truncated Denoising Diffusion Model for super-resolution Magnetic Resonance Spectroscopic Imaging
Dong S, Cai Z, Hangel G, Bogner W, Widhalm G, Huang Y, Liang Q, You C, Kumaragamage C, Fulbright R, Mahajan A, Karbasi A, Onofrey J, de Graaf R, Duncan J. A Flow-based Truncated Denoising Diffusion Model for super-resolution Magnetic Resonance Spectroscopic Imaging. Medical Image Analysis 2024, 99: 103358. PMID: 39353335, PMCID: PMC11609020, DOI: 10.1016/j.media.2024.103358.Peer-Reviewed Original ResearchDenoising diffusion modelsDeep learning-based super-resolution methodsLearning-based super-resolution methodsMulti-scale super-resolutionGenerative modelSuper-resolution methodsDeep learning modelsHigh-resolution magnetic resonance spectroscopic imagingHigh-quality imagesPost-processing approachSuper-resolutionFlow-based networksLearning modelsLow resolutionTruncation stepLow-resolution dataSharpness adjustmentNetworkSensitivity restrictionsUncertainty estimationDiffusion modelImagesCapabilitySampling processSpectroscopic imagingHigh‐resolution extracellular pH imaging of liver cancer with multiparametric MR using Deep Image Prior
Dong S, Shewarega A, Chapiro J, Cai Z, Hyder F, Coman D, Duncan J. High‐resolution extracellular pH imaging of liver cancer with multiparametric MR using Deep Image Prior. NMR In Biomedicine 2024, 37: e5145. PMID: 38488205, DOI: 10.1002/nbm.5145.Peer-Reviewed Original ResearchDeep Image PriorU-NetUnsupervised deep learning techniquesU-Net parametersDeep learning techniquesHigh-resolution ground truthU-Net architectureSuper-resolution imagingImage priorsSuper-resolutionGround truthMean absolute errorDeepSpatial resolutionPostprocessing methodDeep imagingAbsolute errorImagesAnatomical MR imagesMR spectroscopic imagingAnatomical informationSpectroscopic imagingInformationAcquisition timeErrorUnraveling cellular complexity with transient adapters in highly multiplexed super-resolution imaging
Schueder F, Rivera-Molina F, Su M, Marin Z, Kidd P, Rothman J, Toomre D, Bewersdorf J. Unraveling cellular complexity with transient adapters in highly multiplexed super-resolution imaging. Cell 2024, 187: 1769-1784.e18. PMID: 38552613, DOI: 10.1016/j.cell.2024.02.033.Peer-Reviewed Original ResearchConceptsInter-organelle contactsSuper-resolutionMultiplexed super-resolution microscopyIntricate spatial relationshipsGolgi stacksMammalian cellsCellular functionsSuper-resolution microscopyPrimary ciliaSuper-resolution fluorescence microscopyCellular complexityTransient adaptationFluorescence microscopyDNA-PAINTFluorogenic labelingMolecular targetsSpatial relationshipsImagesThroughputLearn from orientation prior for radiograph super-resolution: Orientation operator transformer
Huang Y, Miyazaki T, Liu X, Jiang K, Tang Z, Omachi S. Learn from orientation prior for radiograph super-resolution: Orientation operator transformer. Computer Methods And Programs In Biomedicine 2024, 245: 108000. PMID: 38237449, DOI: 10.1016/j.cmpb.2023.108000.Peer-Reviewed Original ResearchConceptsSingle-image super-resolutionSuper-resolutionFusion strategyMulti-scale feature fusion strategyImage super-resolution taskEffective latent representationsSuper-resolution taskFeature fusion strategyOperational transformationImage enhancement fieldSecond-best performanceLatent representationDenoised mapsUpsampling factorsOrientation operatorObjective metricsColor spaceImaging pipelineHigh-resolution radiographic imagesReceptive fieldsImage qualityExperimental resultsImaging fieldModel performanceImages
2023
Super-resolution in brain positron emission tomography using a real-time motion capture system
Chemli Y, Tétrault M, Marin T, Normandin M, Bloch I, El Fakhri G, Ouyang J, Petibon Y. Super-resolution in brain positron emission tomography using a real-time motion capture system. NeuroImage 2023, 272: 120056. PMID: 36977452, PMCID: PMC10122782, DOI: 10.1016/j.neuroimage.2023.120056.Peer-Reviewed Original ResearchConceptsBrain positron emission tomographySuper-resolutionEvent-by-event basisReal-time motion capture systemSR reconstruction methodTracking cameraVisualization of small structuresPET reconstruction algorithmMoving phantomMeasure target motionLine profilesPET/CT scannerMeasured shiftsImprove image resolutionMotion capture systemMotion tracking devicePositron emission tomographyReconstruction algorithmSpatial resolutionMeasured linesPhantomReal-timeEstimation frameworkIncreased spatial resolutionReconstruction method
2020
Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-scale Generative Adversarial Network
Cui J, Gong K, Han P, Liu H, Li Q. Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-scale Generative Adversarial Network. Lecture Notes In Computer Science 2020, 12436: 50-59. DOI: 10.1007/978-3-030-59861-7_6.Peer-Reviewed Original ResearchPeak signal-to-noise ratioGenerative adversarial networkStructural similarity indexMulti-scale generative adversarial networkSignal-to-noise ratioSuper-resolutionAdversarial networkHigher peak signal-to-noise ratioLow resolutionSuper-resolution methodsLow signal-to-noise ratioUnsupervised trainingPre-trainingWhole tissue volumeNoise interferenceGround truthSpline interpolation methodSimilarity indexConventional ASLNetworkImage noiseInterpolation methodLong acquisition timesHigh-resolutionStructural information
2019
PET Image Deblurring and Super-Resolution With an MR-Based Joint Entropy Prior
Song T, Yang F, Chowdhury S, Kim K, Johnson K, Fakhri G, Li Q, Dutta J. PET Image Deblurring and Super-Resolution With an MR-Based Joint Entropy Prior. IEEE Transactions On Computational Imaging 2019, 5: 530-539. PMID: 31723575, PMCID: PMC6853071, DOI: 10.1109/tci.2019.2913287.Peer-Reviewed Original ResearchContrast-to-noise ratioStructural similarity indexHoffman phantomImage deblurringDigital phantomPhantom studyPeak signal-to-noise ratioSuper-resolution frameworkQuantitative accuracySimilarity indexSignal-to-noise ratioSpatial resolutionPhantomImage quantitationSuper-resolutionTau imaging studiesImage qualityPET imagingDeblurringHigh-resolution MR imagingRoot mean square errorSimulation studyBrainWebPost-processingPenalty function
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