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 2651–2660 of 3874 papers

TitleStatusHype
Transformers in Vision: A Survey—0
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learningCode1
Context Reasoning Attention Network for Image Super-Resolution—0
Real-World Video Super-Resolution: A Benchmark Dataset and a Decomposition Based Learning SchemeCode1
SIGNET: Efficient Neural Representation for Light Fields—0
Event Stream Super-Resolution via Spatiotemporal Constraint Learning—0
Benchmarking Ultra-High-Definition Image Super-Resolution—0
EvIntSR-Net: Event Guided Multiple Latent Frames Reconstruction and Super-Resolution—0
Super Resolve Dynamic Scene From Continuous Spike Streams—0
IntraTomo: Self-Supervised Learning-Based Tomography via Sinogram Synthesis and PredictionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified