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 2431–2440 of 3874 papers

TitleStatusHype
Navigating Beyond Dropout: An Intriguing Solution Towards Generalizable Image Super Resolution—0
NAWQ-SR: A Hybrid-Precision NPU Engine for Efficient On-Device Super-Resolution—0
UB-FineNet: Urban Building Fine-grained Classification Network for Open-access Satellite Images—0
UCIP: A Universal Framework for Compressed Image Super-Resolution using Dynamic Prompt—0
Near-Field ISAC in 6G: Addressing Phase Nonlinearity via Lifted Super-Resolution—0
Nearly optimal resolution estimate for the two-dimensional super-resolution and a new algorithm for direction of arrival estimation with uniform rectangular array—0
Near-realtime Facial Animation by Deep 3D Simulation Super-Resolution—0
Needle-Match: Reliable Patch Matching Under High Uncertainty—0
NegVSR: Augmenting Negatives for Generalized Noise Modeling in Real-World Video Super-Resolution—0
Composite Optimization by Nonconvex Majorization-Minimization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified