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De-aliasing
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De-aliasing is the problem of recovering the original high-frequency information that has been aliased during the acquisition of an image.
논문
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EMWaveNet: Physically Explainable Neural Network Based on Electromagnetic Propagation for SAR Target Recognition
eGAD! double descent is explained by Generalized Aliasing Decomposition
Spectral-Refiner: Accurate Fine-Tuning of Spatiotemporal Fourier Neural Operator for Turbulent Flows
When Semantic Segmentation Meets Frequency Aliasing
DARCS: Memory-Efficient Deep Compressed Sensing Reconstruction for Acceleration of 3D Whole-Heart Coronary MR Angiography
A plug-and-play synthetic data deep learning for undersampled magnetic resonance image reconstruction
A Deep Learning Approach for Parallel Imaging and Compressed Sensing MRI Reconstruction
Multi-branch Cascaded Swin Transformers with Attention to k-space Sampling Pattern for Accelerated MRI Reconstruction
Adaptive Diffusion Priors for Accelerated MRI Reconstruction
Seeking Common Ground While Reserving Differences: Multiple Anatomy Collaborative Framework for Undersampled MRI Reconstruction
A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoisers
Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction
Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations
Temporal Embeddings and Transformer Models for Narrative Text Understanding
HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
RODEO: Robust DE-aliasing autoencOder for Real-time Medical Image Reconstruction
Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging
Can learning from natural image denoising be used for seismic data interpolation?
Real-time Cardiovascular MR with Spatio-temporal Artifact Suppression using Deep Learning - Proof of Concept in Congenital Heart Disease
Deep De-Aliasing for Fast Compressive Sensing MRI
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