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De-aliasing

De-aliasing is the problem of recovering the original high-frequency information that has been aliased during the acquisition of an image.

Papers

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Showing 11–20 of 22 papers

TitleStatusHype
EMWaveNet: Physically Explainable Neural Network Based on Electromagnetic Propagation for SAR Target Recognition—0
eGAD! double descent is explained by Generalized Aliasing Decomposition—0
Temporal Embeddings and Transformer Models for Narrative Text Understanding—0
A Deep Learning Approach for Parallel Imaging and Compressed Sensing MRI Reconstruction—0
Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging—0
Multi-branch Cascaded Swin Transformers with Attention to k-space Sampling Pattern for Accelerated MRI Reconstruction—0
Real-time Cardiovascular MR with Spatio-temporal Artifact Suppression using Deep Learning - Proof of Concept in Congenital Heart Disease—0
A plug-and-play synthetic data deep learning for undersampled magnetic resonance image reconstruction—0
RODEO: Robust DE-aliasing autoencOder for Real-time Medical Image Reconstruction—0
Seeking Common Ground While Reserving Differences: Multiple Anatomy Collaborative Framework for Undersampled MRI Reconstruction—0
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