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 1501–1510 of 3874 papers

TitleStatusHype
Mimic3D: Thriving 3D-Aware GANs via 3D-to-2D Imitation—0
Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection StrategyCode1
Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data OverfittingCode1
Spherical Space Feature Decomposition for Guided Depth Map Super-ResolutionCode0
ViTO: Vision Transformer-Operator—0
DeblurSR: Event-Based Motion Deblurring Under the Spiking RepresentationCode1
Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation—0
Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion ModelsCode1
ResDiff: Combining CNN and Diffusion Model for Image Super-ResolutionCode1
Synthesizing Realistic Image Restoration Training Pairs: A Diffusion Approach—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified