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 3461–3470 of 3874 papers

TitleStatusHype
Learning to synthesize: splitting and recombining low and high spatial frequencies for image recovery—0
Single Snapshot Super-Resolution DOA Estimation for Arbitrary Array Geometries—0
A Learning-Based Framework for Line-Spectra Super-resolutionCode0
LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering—0
Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution NetworkCode0
Functional Nonlinear Sparse Models—0
Bi-GANs-ST for Perceptual Image Super-resolution—0
Neural Nearest Neighbors NetworksCode0
DeepDPM: Dynamic Population Mapping via Deep Neural Network—0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and EnhancementCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified