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 3051–3075 of 3874 papers

TitleStatusHype
Efficient and Phase-aware Video Super-resolution for Cardiac MRI—0
Single Image Super-Resolution via Residual Neuron Attention Networks—0
Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation—0
Enhancing Perceptual Loss with Adversarial Feature Matching for Super-Resolution—0
A Generative Model for Generic Light Field Reconstruction—0
AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results—0
NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results—0
Joint-SRVDNet: Joint Super Resolution and Vehicle Detection Network—0
Neural Differential Equations for Single Image Super-resolution—0
PCA-SRGAN: Incremental Orthogonal Projection Discrimination for Face Super-resolution—0
Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction—0
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP—0
Attention Based Real Image RestorationCode0
Radar Accurate Localization of UAV Swarms Based on Range Super-Resolution Method—0
Mining self-similarity: Label super-resolution with epitomic representationsCode0
RAIN: A Simple Approach for Robust and Accurate Image Classification NetworksCode0
SimUSR: A Simple but Strong Baseline for Unsupervised Image Super-resolution—0
Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization—0
Single Pair Cross-Modality Super Resolution—0
ImagePairs: Realistic Super Resolution Dataset via Beam Splitter Camera Rig—0
Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations—0
Mosaic Super-resolution via Sequential Feature Pyramid Networks—0
Multi-modal Datasets for Super-resolution—0
D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks—0
Image super-resolution reconstruction based on attention mechanism and feature fusion—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified