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 491–500 of 3874 papers

TitleStatusHype
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