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 2051–2100 of 3874 papers

TitleStatusHype
Dynamic super-resolution in particle tracking problems—0
Dynamic Non-Regular Sampling Sensor Using Frequency Selective Reconstruction—0
Super-resolved multi-temporal segmentation with deep permutation-invariant networks—0
A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts—0
Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution—0
Single Image Internal Distribution Measurement Using Non-Local Variational Autoencoder—0
Bayesian Image Super-Resolution with Deep Modeling of Image StatisticsCode1
MyStyle: A Personalized Generative Prior—0
Physics-informed deep-learning applications to experimental fluid mechanics—0
Cross-Modality High-Frequency Transformer for MR Image Super-Resolution—0
Angular Super-Resolution in Diffusion MRI with a 3D Recurrent Convolutional AutoencoderCode1
Reference-based Video Super-Resolution Using Multi-Camera Video TripletsCode2
HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars—0
Neural Vocoder is All You Need for Speech Super-resolutionCode1
Efficient and Degradation-Adaptive Network for Real-World Image Super-ResolutionCode1
A Survey of Super-Resolution in Iris Biometrics with Evaluation of Dictionary-Learning—0
RSTT: Real-time Spatial Temporal Transformer for Space-Time Video Super-ResolutionCode1
Learning Graph Regularisation for Guided Super-ResolutionCode1
Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolutionCode1
NUNet: Deep Learning for Non-Uniform Super-Resolution of Turbulent Flows—0
MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion—0
Increasing the accuracy and resolution of precipitation forecasts using deep generative modelsCode1
Adaptive Patch Exiting for Scalable Single Image Super-ResolutionCode1
ARM: Any-Time Super-Resolution MethodCode1
Optical Flow for Video Super-Resolution: A Survey—0
SDOA-Net: An Efficient Deep Learning-Based DOA Estimation Network for Imperfect ArrayCode1
HIPA: Hierarchical Patch Transformer for Single Image Super Resolution—0
A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolutionCode1
Frequency-Selective Mesh-to-Mesh Resampling for Color Upsampling of Point Clouds—0
Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-ResolutionCode2
Image Super-Resolution With Deep Variational Autoencoders—0
A Novel End-To-End Network for Reconstruction of Non-Regularly Sampled Image Data Using Locally Fully Connected LayersCode0
Towards True Detail Restoration for Super-Resolution: A Benchmark and a Quality Metric—0
Panini-Net: GAN Prior Based Degradation-Aware Feature Interpolation for Face RestorationCode1
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element NetworksCode1
Hybrid Pixel-Unshuffled Network for Lightweight Image Super-ResolutionCode1
Neural RF SLAM for unsupervised positioning and mapping with channel state information—0
Enriched CNN-Transformer Feature Aggregation Networks for Super-ResolutionCode1
Key Point Agnostic Frequency-Selective Mesh-to-Grid Image Resampling using Spectral Weighting—0
STDAN: Deformable Attention Network for Space-Time Video Super-ResolutionCode1
GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors—0
Efficient Long-Range Attention Network for Image Super-resolutionCode2
Unfolded Deep Kernel Estimation for Blind Image Super-resolutionCode1
Manifold Modeling in Quotient Space: Learning An Invariant Mapping with Decodability of Image Patches—0
Learning the Degradation Distribution for Blind Image Super-ResolutionCode1
Rethinking data-driven point spread function modeling with a differentiable optical modelCode1
Regularized Training of Intermediate Layers for Generative Models for Inverse ProblemsCode0
Fast and selective super-resolution ultrasound in vivo with sono-switchable nanodroplets—0
Sub-Terahertz Channel Measurements and Characterization in a Factory Building—0
Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution NetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified