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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.

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Greatest papers with code

HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery

15 Feb 2020ElementAI/HighRes-net

Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.

DE-ALIASING IMAGE REGISTRATION MULTI-FRAME SUPER-RESOLUTION

HighRes-net: Multi-Frame Super-Resolution by Recursive Fusion

ICLR 2020 ElementAI/HighRes-net

Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.

DE-ALIASING IMAGE REGISTRATION MULTI-FRAME SUPER-RESOLUTION

Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction

22 Dec 2020cq615/kt-Dynamic-MRI-Reconstruction

The iterative model is embedded into a deep recurrent neural network which learns to recover the image via exploiting spatio-temporal redundancies in complementary domains.

DE-ALIASING IMAGE RECONSTRUCTION

Temporal Embeddings and Transformer Models for Narrative Text Understanding

19 Mar 2020IDSIA/novel2graph

We present two deep learning approaches to narrative text understanding for character relationship modelling.

DE-ALIASING NATURAL LANGUAGE UNDERSTANDING WORD EMBEDDINGS

Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations

27 Jun 2020edongdongchen/PGD-Net

Consistency of the predictions with respect to the physical forward model is pivotal for reliably solving inverse problems.

DE-ALIASING MAGNETIC RESONANCE FINGERPRINTING

Can learning from natural image denoising be used for seismic data interpolation?

27 Feb 2019AlbertZhangHIT/CNN-POCS

We propose a convolutional neural network (CNN) denoising based method for seismic data interpolation.

DE-ALIASING IMAGE DENOISING