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 1351–1360 of 3874 papers

TitleStatusHype
A Unified Framework to Super-Resolve Face Images of Varied Low Resolutions—0
Model-Based Deep LearningCode1
SwinRDM: Integrate SwinRNN with Diffusion Model towards High-Resolution and High-Quality Weather Forecasting—0
AI Techniques for Cone Beam Computed Tomography in Dentistry: Trends and Practices—0
Scale Guided Hypernetwork for Blind Super-Resolution Image Quality AssessmentCode0
ESTISR: Adapting Efficient Scene Text Image Super-resolution for Real-Scenes—0
EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection—0
A Feature Reuse Framework with Texture-adaptive Aggregation for Reference-based Super-ResolutionCode1
Towards Real-Time 4K Image Super-ResolutionCode1
Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and ReportCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified