De-aliasing

6 papers with code • 0 benchmarks • 0 datasets

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

Most implemented papers

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

AlbertZhangHIT/CNN-POCS 27 Feb 2019

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

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

ElementAI/HighRes-net ICLR 2020

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

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

ElementAI/HighRes-net 15 Feb 2020

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

Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations

edongdongchen/PGD-Net 27 Jun 2020

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

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

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

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.

A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoisers

ketanfatania/qmri-pnp-recon-poc 10 Feb 2022

This paper proposes an iterative deep learning plug-and-play reconstruction approach to MRF which is adaptive to the forward acquisition process.