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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 1–10 of 22 papers

TitleStatusHype
Deep learning based spatial aliasing reduction in beamforming for audio capture—0
EMWaveNet: Physically Explainable Neural Network Based on Electromagnetic Propagation for SAR Target Recognition—0
eGAD! double descent is explained by Generalized Aliasing Decomposition—0
Spectral-Refiner: Accurate Fine-Tuning of Spatiotemporal Fourier Neural Operator for Turbulent FlowsCode2
When Semantic Segmentation Meets Frequency AliasingCode1
DARCS: Memory-Efficient Deep Compressed Sensing Reconstruction for Acceleration of 3D Whole-Heart Coronary MR Angiography—0
A plug-and-play synthetic data deep learning for undersampled magnetic resonance image reconstruction—0
A Deep Learning Approach for Parallel Imaging and Compressed Sensing MRI Reconstruction—0
Multi-branch Cascaded Swin Transformers with Attention to k-space Sampling Pattern for Accelerated MRI Reconstruction—0
Adaptive Diffusion Priors for Accelerated MRI ReconstructionCode1
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