Image Reconstruction
468 papers with code • 5 benchmarks • 7 datasets
Benchmarks
These leaderboards are used to track progress in Image Reconstruction
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Libraries
Use these libraries to find Image Reconstruction models and implementations
General-100
CED
SEN12MS-CR
SEN12MS-CR-TS
Most implemented papers
Unsupervised Monocular Depth Estimation with Left-Right Consistency
mrharicot/monodepth
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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
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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
Per-pixel ground-truth depth data is challenging to acquire at scale.
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
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
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
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
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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
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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
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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
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A combination of Deep CNNs and Skip connection layers is used as a feature extractor for image features on both local and global area.
