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 476–500 of 3874 papers

TitleStatusHype
BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-ResolutionCode1
Dual-Stage Approach Toward Hyperspectral Image Super-ResolutionCode1
Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIsCode1
AeroRIT: A New Scene for Hyperspectral Image AnalysisCode1
Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI ScansCode1
Face Hallucination via Split-Attention in Split-Attention NetworkCode1
Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution NetworksCode1
Context-self contrastive pretraining for crop type semantic segmentationCode1
Event Enhanced High-Quality Image RecoveryCode1
Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image FusionCode1
DynaVSR: Dynamic Adaptive Blind Video Super-ResolutionCode1
ECAMP: Entity-centered Context-aware Medical Vision Language Pre-trainingCode1
EBSR: Feature Enhanced Burst Super-Resolution With Deformable AlignmentCode1
Image super-resolution via dynamic networkCode1
Image Super-Resolution via Iterative RefinementCode1
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo SimulationCode1
Edge and Identity Preserving Network for Face Super-ResolutionCode1
Image Super-Resolution with Deep DictionaryCode1
Edge-enhanced Feature Distillation Network for Efficient Super-ResolutionCode1
Consistent Direct Time-of-Flight Video Depth Super-ResolutionCode1
EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial LearningCode1
Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model InferenceCode1
EDVR: Video Restoration with Enhanced Deformable Convolutional NetworksCode1
Conffusion: Confidence Intervals for Diffusion ModelsCode1
A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified