Image Reconstruction

326 papers with code • 4 benchmarks • 7 datasets

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Libraries

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.

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.

Towards real-time unsupervised monocular depth estimation on CPU

mattpoggi/pydnet 29 Jun 2018

To tackle this issue, in this paper we propose a novel architecture capable to quickly infer an accurate depth map on a CPU, even of an embedded system, using a pyramid of features extracted from a single input image.

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.

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 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.

Efficient and accurate inversion of multiple scattering with deep learning

wustl-cig/ScatteringDecoder 18 Mar 2018

Image reconstruction under multiple light scattering is crucial in a number of applications such as diffraction tomography.