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 2961–2970 of 3874 papers

TitleStatusHype
V2X Sidelink Positioning in FR1: From Ray-Tracing and Channel Estimation to Bayesian Tracking—0
Variational Message Passing-based Multiobject Tracking for MIMO-Radars using Raw Sensor Signals—0
VarSR: Variational Super-Resolution Network for Very Low Resolution Images—0
VEnhancer: Generative Space-Time Enhancement for Video Generation—0
Verification of Very Low-Resolution Faces Using An Identity-Preserving Deep Face Super-Resolution Network—0
Very Deep Super-Resolution of Remotely Sensed Images with Mean Square Error and Var-norm Estimators as Loss Functions—0
Very Low-Resolution Iris Recognition Via Eigen-Patch Super-Resolution and Matcher Fusion—0
VESR-Net: The Winning Solution to Youku Video Enhancement and Super-Resolution Challenge—0
VFHQ: A High-Quality Dataset and Benchmark for Video Face Super-Resolution—0
VHS to HDTV Video Translation using Multi-task Adversarial Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified