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 2611–2620 of 3874 papers

TitleStatusHype
Spatio-Temporal Super-Resolution of Dynamical Systems using Physics-Informed Deep-Learning—0
Spatio-temporal Transformer Network for Video Restoration—0
SPDER: Semiperiodic Damping-Enabled Object Representation—0
Spectral Bandwidth Recovery of Optical Coherence Tomography Images using Deep Learning—0
Spectral-based detection of chromatin loops in multiplexed super-resolution FISH data—0
Spectral Bayesian Uncertainty for Image Super-Resolution—0
Spectral Compressed Sensing via Structured Matrix Completion—0
SpectraLift: Physics-Guided Spectral-Inversion Network for Self-Supervised Hyperspectral Image Super-Resolution—0
Spectral Response Function Guided Deep Optimization-driven Network for Spectral Super-resolution—0
From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified