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 521–530 of 3874 papers

TitleStatusHype
Fast and Memory-Efficient Network Towards Efficient Image Super-ResolutionCode1
Fast Monte Carlo Rendering via Multi-Resolution SamplingCode1
FedVSR: Towards Model-Agnostic Federated Learning in Video Super-ResolutionCode1
Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real WorldCode1
Fairness for Image Generation with Uncertain Sensitive AttributesCode1
A Physics-Informed Meta-Learning Framework for the Continuous Solution of Parametric PDEs on Arbitrary GeometriesCode1
Context-self contrastive pretraining for crop type semantic segmentationCode1
Continuous Remote Sensing Image Super-Resolution based on Context Interaction in Implicit Function SpaceCode1
FALL-E: A Foley Sound Synthesis Model and StrategiesCode1
Face Super-Resolution Guided by 3D Facial PriorsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified