Image Reconstruction

202 papers with code • 3 benchmarks • 3 datasets

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Greatest papers with code

Generative Adversarial Network in Medical Imaging: A Review

xinario/awesome-gan-for-medical-imaging 19 Sep 2018

Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function.

Data Augmentation Domain Adaptation +3

PI-REC: Progressive Image Reconstruction Network With Edge and Color Domain

youyuge34/PI-REC arXiv 2019

We propose a universal image reconstruction method to represent detailed images purely from binary sparse edge and flat color domain.

Image Reconstruction Image-to-Image Translation

GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose

yzcjtr/GeoNet CVPR 2018

We propose GeoNet, a jointly unsupervised learning framework for monocular depth, optical flow and ego-motion estimation from videos.

Image Reconstruction Motion Estimation +1

Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction

facebookresearch/fastMRI 9 Dec 2020

Accelerating MRI scans is one of the principal outstanding problems in the MRI research community.

MRI Reconstruction SSIM

Active MR k-space Sampling with Reinforcement Learning

facebookresearch/fastMRI 20 Jul 2020

Deep learning approaches have recently shown great promise in accelerating magnetic resonance image (MRI) acquisition.

Image Reconstruction

Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge

facebookresearch/fastMRI 6 Jan 2020

Conclusion: The challenge led to new developments in machine learning for image reconstruction, provided insight into the current state of the art in the field, and highlighted remaining hurdles for clinical adoption.

Image Reconstruction

fastMRI: An Open Dataset and Benchmarks for Accelerated MRI

facebookresearch/fastMRI 21 Nov 2018

Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive.

Image Reconstruction

Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network

jiny2001/dcscn-super-resolution 18 Jul 2017

A combination of Deep CNNs and Skip connection layers is used as a feature extractor for image features on both local and global area.

Image Reconstruction Image Super-Resolution