Papers De-aliasing
“De-aliasing” 태그가 달린 논문 22편 · 필터 해제
Deep learning based spatial aliasing reduction in beamforming for audio capture
Spatial aliasing affects spaced microphone arrays, causing directional ambiguity above certain frequencies, degrading spatial and spectral accuracy of beamformers. Given the limitations of conventional signal processing …
De-aliasingDeep LearningEMWaveNet: Physically Explainable Neural Network Based on Electromagnetic Propagation for SAR Target Recognition
Deep learning technologies have significantly improved performance in the field of synthetic aperture radar (SAR) image target recognition compared to traditional methods. However, the inherent ``black box" property of d…
De-aliasingeGAD! double descent is explained by Generalized Aliasing Decomposition
A central problem in data science is to use potentially noisy samples of an unknown function to predict values for unseen inputs. In classical statistics, predictive error is understood as a trade-off between the bias an…
De-aliasingExperimental DesignModel SelectionSpectral-Refiner: Accurate Fine-Tuning of Spatiotemporal Fourier Neural Operator for Turbulent Flows
Recent advancements in operator-type neural networks have shown promising results in approximating the solutions of spatiotemporal Partial Differential Equations (PDEs). However, these neural networks often entail consid…
Computational EfficiencyDe-aliasingOperator learningSuper-ResolutionWhen Semantic Segmentation Meets Frequency Aliasing
Despite recent advancements in semantic segmentation, where and what pixels are hard to segment remains largely unexplored. Existing research only separates an image into easy and hard regions and empirically observes th…
De-aliasingInstance SegmentationSegmentationSemantic SegmentationDARCS: Memory-Efficient Deep Compressed Sensing Reconstruction for Acceleration of 3D Whole-Heart Coronary MR Angiography
Three-dimensional coronary magnetic resonance angiography (CMRA) demands reconstruction algorithms that can significantly suppress the artifacts from a heavily undersampled acquisition. While unrolling-based deep reconst…
3D Reconstructioncompressed sensingDe-aliasingGenerative Adversarial Network+1A plug-and-play synthetic data deep learning for undersampled magnetic resonance image reconstruction
Magnetic resonance imaging (MRI) plays an important role in modern medical diagnostic but suffers from prolonged scan time. Current deep learning methods for undersampled MRI reconstruction exhibit good performance in im…
De-aliasingDiagnosticImage ReconstructionMRI ReconstructionA Deep Learning Approach for Parallel Imaging and Compressed Sensing MRI Reconstruction
Parallel imaging accelerates MRI data acquisition by acquiring additional sensitivity information with an array of receiver coils, resulting in fewer phase encoding steps. Because of fewer data requirements than parallel…
compressed sensingDe-aliasingGenerative Adversarial NetworkMRI ReconstructionMulti-branch Cascaded Swin Transformers with Attention to k-space Sampling Pattern for Accelerated MRI Reconstruction
Global correlations are widely seen in human anatomical structures due to similarity across tissues and bones. These correlations are reflected in magnetic resonance imaging (MRI) scans as a result of close-range proton …
De-aliasingMRI ReconstructionAdaptive Diffusion Priors for Accelerated MRI Reconstruction
Deep MRI reconstruction is commonly performed with conditional models that de-alias undersampled acquisitions to recover images consistent with fully-sampled data. Since conditional models are trained with knowledge of t…
De-aliasingMRI ReconstructionSeeking Common Ground While Reserving Differences: Multiple Anatomy Collaborative Framework for Undersampled MRI Reconstruction
Recently, deep neural networks have greatly advanced undersampled Magnetic Resonance Image (MRI) reconstruction, wherein most studies follow the one-anatomy-one-network fashion, i.e., each expert network is trained and e…
AnatomyDe-aliasingMRI ReconstructionA Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoisers
Current spatiotemporal deep learning approaches to Magnetic Resonance Fingerprinting (MRF) build artefact-removal models customised to a particular k-space subsampling pattern which is used for fast (compressed) acquisit…
De-aliasingDeep LearningImage ReconstructionMagnetic Resonance Fingerprinting+1Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction
Purpose: To introduce a novel deep learning based approach for fast and high-quality dynamic multi-coil MR reconstruction by learning a complementary time-frequency domain network that exploits spatio-temporal correlatio…
De-aliasingImage ReconstructionCompressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations
Consistency of the predictions with respect to the physical forward model is pivotal for reliably solving inverse problems. This consistency is mostly un-controlled in the current end-to-end deep learning methodologies p…
De-aliasingDeep LearningMagnetic Resonance FingerprintingTemporal Embeddings and Transformer Models for Narrative Text Understanding
We present two deep learning approaches to narrative text understanding for character relationship modelling. The temporal evolution of these relations is described by dynamic word embeddings, that are designed to learn …
ClusteringDe-aliasingDiachronic Word EmbeddingsNatural Language Understanding+1HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details. Multi-frame Super-Resolution (MFSR) offers …
De-aliasingEarth ObservationImage RegistrationMulti-Frame Super-Resolution+1HighRes-net: Multi-Frame Super-Resolution by Recursive Fusion
Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details. Multi-frame Super-Resolution (MFSR) offers …
De-aliasingEarth ObservationImage RegistrationImage Super-Resolution+2RODEO: Robust DE-aliasing autoencOder for Real-time Medical Image Reconstruction
In this work we address the problem of real-time dynamic medical MRI and X Ray CT image reconstruction from parsimonious samples Fourier frequency space for MRI and sinogram tomographic projections for CT. Today the de f…
compressed sensingDe-aliasingImage ReconstructionModel-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging
Parallel imaging has been an essential technique to accelerate MR imaging. Nevertheless, the acceleration rate is still limited due to the ill-condition and challenges associated with the undersampled reconstruction. In …
De-aliasingSensitivityCan learning from natural image denoising be used for seismic data interpolation?
We propose a convolutional neural network (CNN) denoising based method for seismic data interpolation. It provides a simple and efficient way to break though the lack problem of geophysical training labels that are often…
De-aliasingDenoisingImage Denoising