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 431–440 of 3874 papers

TitleStatusHype
HQ-50K: A Large-scale, High-quality Dataset for Image RestorationCode1
Model-Based Deep LearningCode1
A Feature Reuse Framework with Texture-adaptive Aggregation for Reference-based Super-ResolutionCode1
Symmetric Uncertainty-Aware Feature Transmission for Depth Super-ResolutionCode1
Towards Real-Time 4K Image Super-ResolutionCode1
Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and ReportCode1
Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution LayersCode1
Accelerating Diffusion Models for Inverse Problems through Shortcut SamplingCode1
GenerateCT: Text-Conditional Generation of 3D Chest CT VolumesCode1
EgoVSR: Towards High-Quality Egocentric Video Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified