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 511–520 of 3874 papers

TitleStatusHype
Enhanced Super-Resolution Training via Mimicked Alignment for Real-World ScenesCode1
A Unified Model for Compressed Sensing MRI Across Undersampling Patterns—0
TV-based Deep 3D Self Super-Resolution for fMRI—0
Distillation-Free One-Step Diffusion for Real-World Image Super-ResolutionCode2
AIM 2024 Challenge on Video Super-Resolution Quality Assessment: Methods and Results—0
Exploring Strengths and Weaknesses of Super-Resolution Attack in Deepfake Detection—0
Learning Truncated Causal History Model for Video RestorationCode2
PnP-Flow: Plug-and-Play Image Restoration with Flow MatchingCode2
PixelShuffler: A Simple Image Translation Through Pixel RearrangementCode1
SuperGS: Super-Resolution 3D Gaussian Splatting Enhanced by Variational Residual Features and Uncertainty-Augmented LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified