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 851–875 of 3874 papers

TitleStatusHype
EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-ResolutionCode1
edge-SR: Super-Resolution For The MassesCode1
BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural NetworksCode1
EDPN: Enhanced Deep Pyramid Network for Blurry Image RestorationCode1
Deep Learning-Driven Ultra-High-Definition Image Restoration: A SurveyCode1
Plug-and-Play Image Restoration with Deep Denoiser PriorCode1
Burst Image Restoration and EnhancementCode1
Edge and Identity Preserving Network for Face Super-ResolutionCode1
Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS DataCode1
Preserving Full Degradation Details for Blind Image Super-ResolutionCode1
Principal Uncertainty Quantification with Spatial Correlation for Image Restoration ProblemsCode1
Edge-enhanced Feature Distillation Network for Efficient Super-ResolutionCode1
EDVR: Video Restoration with Enhanced Deformable Convolutional NetworksCode1
Adaptive Local Implicit Image Function for Arbitrary-scale Super-resolutionCode1
EBSR: Feature Enhanced Burst Super-Resolution With Deformable AlignmentCode1
Dynamic Implicit Image Function for Efficient Arbitrary-Scale Image RepresentationCode1
Diffusion Models Beat GANs on Image ClassificationCode1
Pyramid With Super Resolution for In-the-Wild Facial Expression RecognitionCode1
Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-ResolutionCode1
QDM: Quadtree-Based Region-Adaptive Sparse Diffusion Models for Efficient Image Super-ResolutionCode1
Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modellingCode1
Accelerating Diffusion Models for Inverse Problems through Shortcut SamplingCode1
Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluationCode1
Rank-One Network: An Effective Framework for Image RestorationCode1
BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable AlignmentCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified