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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2126–2150 of 3874 papers

TitleStatusHype
Evaluating Detection Thresholds: The Impact of False Positives and Negatives on Super-Resolution Ultrasound Localization Microscopy—0
Evaluating Loss Functions and Learning Data Pre-Processing for Climate Downscaling Deep Learning Models—0
Evaluating the Adversarial Robustness for Fourier Neural Operators—0
Evaluating the Generalization Ability of Super-Resolution Networks—0
Evaluation of Machine-generated Biomedical Images via A Tally-based Similarity Measure—0
EventAid: Benchmarking Event-aided Image/Video Enhancement Algorithms with Real-captured Hybrid Dataset—0
Event-based Video Super-Resolution via State Space Models—0
Event Signal Filtering via Probability Flux Estimation—0
Event-Stream Super Resolution using Sigma-Delta Neural Network—0
Event Stream Super-Resolution via Spatiotemporal Constraint Learning—0
Every Pixel Tells a Story: End-to-End Urdu Newspaper OCR—0
EvIntSR-Net: Event Guided Multiple Latent Frames Reconstruction and Super-Resolution—0
EVRNet: Efficient Video Restoration on Edge Devices—0
Example-Based Modeling of Facial Texture From Deficient Data—0
Example-based super-resolution for point-cloud video—0
Expanding Synthetic Real-World Degradations for Blind Video Super Resolution—0
Expansion microscopy reveals neural circuit organization in genetic animal models—0
Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions—0
Explanatory Analysis and Rectification of the Pitfalls in COVID-19 Datasets—0
Exploiting Digital Surface Models for Inferring Super-Resolution for Remotely Sensed Images—0
Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models—0
What's in the Image? Explorable Decoding of Compressed Images—0
Exploring Deep Learning Image Super-Resolution for Iris Recognition—0
Exploring Diffusion with Test-Time Training on Efficient Image Restoration—0
Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified