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 3201–3210 of 3874 papers

TitleStatusHype
Analysis Operator Learning and Its Application to Image Reconstruction—0
Spatial-Temporal Space Hand-in-Hand: Spatial-Temporal Video Super-Resolution via Cycle-Projected Mutual Learning—0
Analysis and Interpretation of Deep CNN Representations as Perceptual Quality Features—0
Video Interpolation with Diffusion Models—0
Generative AI Enables EEG Super-Resolution via Spatio-Temporal Adaptive Diffusion Learning—0
ZipNet-GAN: Inferring Fine-grained Mobile Traffic Patterns via a Generative Adversarial Neural Network—0
Video Restoration with a Deep Plug-and-Play Prior—0
Spatio-Temporal Super-Resolution of Dynamical Systems using Physics-Informed Deep-Learning—0
Spatio-temporal Transformer Network for Video Restoration—0
Analog Neural Computing with Super-resolution Memristor Crossbars—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified