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 481–490 of 3874 papers

TitleStatusHype
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
Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion ModelsCode1
DeblurSR: Event-Based Motion Deblurring Under the Spiking RepresentationCode1
ResDiff: Combining CNN and Diffusion Model for Image Super-ResolutionCode1
Super-Resolution Information Enhancement For Crowd CountingCode1
Recursive Generalization Transformer for Image Super-ResolutionCode1
Generative AI for Rapid Diffusion MRI with Improved Image Quality, Reliability and GeneralizabilityCode1
LMR: A Large-Scale Multi-Reference Dataset for Reference-based Super-ResolutionCode1
Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified