Image Reconstruction

468 papers with code • 5 benchmarks • 7 datasets

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Use these libraries to find Image Reconstruction models and implementations

Most implemented papers

Unsupervised Monocular Depth Estimation with Left-Right Consistency

mrharicot/monodepth CVPR 2017

Learning based methods have shown very promising results for the task of depth estimation in single images.

Universal Style Transfer via Feature Transforms

Yijunmaverick/UniversalStyleTransfer NeurIPS 2017

The whitening and coloring transforms reflect a direct matching of feature covariance of the content image to a given style image, which shares similar spirits with the optimization of Gram matrix based cost in neural style transfer.

Digging Into Self-Supervised Monocular Depth Estimation

nianticlabs/monodepth2 4 Jun 2018

Per-pixel ground-truth depth data is challenging to acquire at scale.

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.

SwinIR: Image Restoration Using Swin Transformer

jingyunliang/swinir 23 Aug 2021

In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection.

Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks

Araxeus/PNG-Upscale 4 Oct 2017

However, existing methods often require a large number of network parameters and entail heavy computational loads at runtime for generating high-accuracy super-resolution results.

Homotopic Gradients of Generative Density Priors for MR Image Reconstruction

yqx7150/HGGDP 14 Aug 2020

Deep learning, particularly the generative model, has demonstrated tremendous potential to significantly speed up image reconstruction with reduced measurements recently.

A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction

js3611/Deep-MRI-Reconstruction 1 Mar 2017

The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow.

A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction

js3611/Deep-MRI-Reconstruction 8 Apr 2017

Firstly, we show that when each 2D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2D compressed sensing approaches such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed.

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.