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 2221–2230 of 3874 papers

TitleStatusHype
Rethinking Image Super Resolution From Long-Tailed Distribution Learning Perspective—0
Pyramid Dual Domain Injection Network for Pan-sharpening—0
Metadata-Based RAW Reconstruction via Implicit Neural Functions—0
Memory-Friendly Scalable Super-Resolution via Rewinding Lottery Ticket Hypothesis—0
Cross-Guided Optimization of Radiance Fields With Multi-View Image Super-Resolution for High-Resolution Novel View Synthesis—0
DIVA: Deep Unfolded Network from Quantum Interactive Patches for Image Restoration—0
Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid NetworkCode0
Large-scale single-photon imaging—0
Single-Image Super-Resolution Reconstruction based on the Differences of Neighboring Pixels—0
Transformer and GAN Based Super-Resolution Reconstruction Network for Medical Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified