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 421–430 of 3874 papers

TitleStatusHype
BAM: A Balanced Attention Mechanism for Single Image Super ResolutionCode1
Event Enhanced High-Quality Image RecoveryCode1
Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluationCode1
Detail-Preserving Transformer for Light Field Image Super-ResolutionCode1
edge-SR: Super-Resolution For The MassesCode1
BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural NetworksCode1
Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object DetectionCode1
Exploring Semantic Feature Discrimination for Perceptual Image Super-Resolution and Opinion-Unaware No-Reference Image Quality AssessmentCode1
Exploring the Low-Pass Filtering Behavior in Image Super-ResolutionCode1
Decomposition-Based Variational Network for Multi-Contrast MRI Super-Resolution and ReconstructionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified