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 111–120 of 3874 papers

TitleStatusHype
Binarized Diffusion Model for Image Super-ResolutionCode2
I^2SB: Image-to-Image Schrödinger BridgeCode2
Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual LossCode2
Image Restoration with Mean-Reverting Stochastic Differential EquationsCode2
Adaptive Super Resolution For One-Shot Talking-Head GenerationCode2
Implicit Diffusion Models for Continuous Super-ResolutionCode2
DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-ResolutionCode2
Bicubic++: Slim, Slimmer, Slimmest -- Designing an Industry-Grade Super-Resolution NetworkCode2
All-In-One Medical Image Restoration via Task-Adaptive RoutingCode2
Denoising Diffusion Models for Plug-and-Play Image RestorationCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified