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 421–430 of 3874 papers

TitleStatusHype
BAM: A Balanced Attention Mechanism for Single Image Super ResolutionCode1
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable DiffusionCode1
Efficient and Degradation-Adaptive Network for Real-World Image Super-ResolutionCode1
Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-ResolutionCode1
A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net DiscriminatorsCode1
BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural NetworksCode1
Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super ResolutionCode1
Deep Model-Based Super-Resolution with Non-uniform BlurCode1
Deep Plug-and-Play Prior for Hyperspectral Image RestorationCode1
Component Divide-and-Conquer for Real-World Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified