SOTAVerified

Super-Resolution

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 )

Papers

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Showing 2951–3000 of 3874 papers

TitleStatusHype
Light Stage Super-Resolution: Continuous High-Frequency Relighting—0
A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery—0
VolumeNet: A Lightweight Parallel Network for Super-Resolution of Medical Volumetric Data—0
Morphological Reconstruction Improves Microvessel Mapping in Super-Resolution Ultrasound—0
AI-assisted super-resolution cosmological simulations—0
LASSR: Effective Super-Resolution Method for Plant Disease Diagnosis—0
Neural Enhancement in Content Delivery Systems: The State-of-the-Art and Future Directions—0
Diagnosing and Preventing Instabilities in Recurrent Video Processing—0
WDN: A Wide and Deep Network to Divide-and-Conquer Image Super-resolution—0
ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-ResolutionCode0
AFN: Attentional Feedback Network based 3D Terrain Super-ResolutionCode0
Spatial Frequency Bias in Convolutional Generative Adversarial Networks—0
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network—0
Deformable Kernel Convolutional Network for Video Extreme Super-Resolution—0
FAN: Frequency Aggregation Network for Real Image Super-resolution—0
AIM 2020 Challenge on Video Temporal Super-Resolution—0
Deep Selective Combinatorial Embedding and Consistency Regularization for Light Field Super-resolution—0
ADMM-Net for Communication Interference Removal in Stepped-Frequency Radar—0
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results—0
Deep Artifact-Free Residual Network for Single Image Super-Resolution—0
Blind Image Super-Resolution with Spatial Context Hallucination—0
Reconstruct high-resolution multi-focal plane images from a single 2D wide field image—0
AdderSR: Towards Energy Efficient Image Super-Resolution—0
DeepRemaster: Temporal Source-Reference Attention Networks for Comprehensive Video Enhancement—0
Multiple Exemplars-based Hallucinationfor Face Super-resolution and Editing—0
Understanding Deformable Alignment in Video Super-Resolution—0
Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labelsCode0
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network—0
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results—0
Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network—0
Cephalogram Synthesis and Landmark Detection in Dental Cone-Beam CT Systems—0
Single Image Super-Resolution for Domain-Specific Ultra-Low Bandwidth Image Transmission—0
not-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution—0
Deep Iterative Residual Convolutional Network for Single Image Super-ResolutionCode0
Interpretable Deep Multimodal Image Super-Resolution—0
Perceptual Deep Neural Networks: Adversarial Robustness through Input Recreation—0
Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS—0
Image Super-Resolution using Explicit Perceptual Loss—0
Accelerated WGAN update strategy with loss change rate balancing—0
Unsupervised MRI Super-Resolution Using Deep External Learning and Guided Residual Dense Network with Multimodal Image Priors—0
Cascade Convolutional Neural Network for Image Super-Resolution—0
PNEN: Pyramid Non-Local Enhanced Networks—0
Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations—0
E-FCNN for tiny facial expression recognition—0
A Study of Efficient Light Field Subsampling and Reconstruction Strategies—0
Transfer Learning for Protein Structure Classification at Low ResolutionCode0
TextureWGAN: Texture Preserving WGAN with MLE Regularizer for Inverse Problems—0
Fusion of Deep and Non-Deep Methods for Fast Super-Resolution of Satellite Images—0
Deep Photo Cropper and Enhancer—0
Binarized Neural Network for Single Image Super Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified