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 1911–1920 of 3874 papers

TitleStatusHype
Untrained, physics-informed neural networks for structured illumination microscopy—0
Perception-Oriented Stereo Image Super-Resolution—0
BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural NetworksCode1
Rethinking Super-Resolution as Text-Guided Details Generation—0
E2FIF: Push the limit of Binarized Deep Imagery Super-resolution using End-to-end Full-precision Information FlowCode0
Rich Feature Distillation with Feature Affinity Module for Efficient Image Dehazing—0
Open High-Resolution Satellite Imagery: The WorldStrat Dataset -- With Application to Super-ResolutionCode2
You Only Align Once: Bidirectional Interaction for Spatial-Temporal Video Super-Resolution—0
Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model—0
Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified