Image Reconstruction
528 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
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.
Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction
js3611/Deep-MRI-Reconstruction
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In particular, the proposed architecture embeds the structure of the traditional iterative algorithms, efficiently modelling the recurrence of the iterative reconstruction stages by using recurrent hidden connections over such iterations.
Efficient and accurate inversion of multiple scattering with deep learning
wustl-cig/ScatteringDecoder
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Image reconstruction under multiple light scattering is crucial in a number of applications such as diffraction tomography.
Towards real-time unsupervised monocular depth estimation on CPU
mattpoggi/pydnet
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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.
Probabilistic Autoencoder
VMBoehm/PAE
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Under review 2020
The PAE is fast and easy to train and achieves small reconstruction errors, high sample quality, and good performance in downstream tasks.
Gradient Origin Networks
This paper proposes a new type of generative model that is able to quickly learn a latent representation without an encoder.
ReconResNet: Regularised Residual Learning for MR Image Reconstruction of Undersampled Cartesian and Radial Data
It has been shown that the proposed framework can successfully reconstruct even for an acceleration factor of 20 for Cartesian (0. 968$\pm$0. 005) and 17 for radially (0. 962$\pm$0. 012) sampled data.
MoDL: Model Based Deep Learning Architecture for Inverse Problems
hkaggarwal/modl
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Since the forward model is explicitly accounted for, a smaller network with fewer parameters is sufficient to capture the image information compared to black-box deep learning approaches, thus reducing the demand for training data and training time.
GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose
We propose GeoNet, a jointly unsupervised learning framework for monocular depth, optical flow and ego-motion estimation from videos.
Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration
LucasSheng/avatar-net
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CVPR 2018
Zero-shot artistic style transfer is an important image synthesis problem aiming at transferring arbitrary style into content images.
