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 631–640 of 3874 papers

TitleStatusHype
Deep Semantic Statistics Matching (D2SM) Denoising NetworkCode1
EvEnhancer: Empowering Effectiveness, Efficiency and Generalizability for Continuous Space-Time Video Super-Resolution with EventsCode1
DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-ResolutionCode1
EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial LearningCode1
Deep Space-Time Video Upsampling NetworksCode1
DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional FlowsCode1
Exploiting Self-Supervised Constraints in Image Super-ResolutionCode1
Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object DetectionCode1
Deep Plug-and-Play Super-Resolution for Arbitrary Blur KernelsCode1
Deep Plug-and-Play Prior for Hyperspectral Image RestorationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified