Super-Resolution
1297 papers with code • 0 benchmarks • 20 datasets
Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.
( Credit: MemNet )
Benchmarks
These leaderboards are used to track progress in Super-Resolution
Datasets
MSU SR-QA Dataset
TextZoom
RealSRSet
DRealSR
Holopix50k
PROBA-V
Stanford Light Field
OST300
Subtasks
Most implemented papers
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images.
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.
"Zero-Shot" Super-Resolution using Deep Internal Learning
On such images, our method outperforms SotA CNN-based SR methods, as well as previous unsupervised SR methods.
Recurrent Back-Projection Network for Video Super-Resolution
alterzero/RBPN-PyTorch
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CVPR 2019
We proposed a novel architecture for the problem of video super-resolution.
Deep Back-Projection Networks for Single Image Super-resolution
Previous feed-forward architectures of recently proposed deep super-resolution networks learn the features of low-resolution inputs and the non-linear mapping from those to a high-resolution output.
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
andreas128/SRFlow
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ECCV 2020
SRFlow therefore directly accounts for the ill-posed nature of the problem, and learns to predict diverse photo-realistic high-resolution images.
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
fangchangma/sparse-to-dense
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We consider the problem of dense depth prediction from a sparse set of depth measurements and a single RGB image.
A Fully Progressive Approach to Single-Image Super-Resolution
fperazzi/proSR
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Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality.
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
kumar-shridhar/PyTorch-BayesianCNN
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In this paper, Bayesian Convolutional Neural Network (BayesCNN) using Variational Inference is proposed, that introduces probability distribution over the weights.
Pre-Trained Image Processing Transformer
huawei-noah/Pretrained-IPT
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CVPR 2021
To maximally excavate the capability of transformer, we present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs.
