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 571–580 of 3874 papers

TitleStatusHype
ControlSR: Taming Diffusion Models for Consistent Real-World Image Super ResolutionCode1
Deep Plug-and-Play Prior for Hyperspectral Image RestorationCode1
Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modellingCode1
Angular Super-Resolution in Diffusion MRI with a 3D Recurrent Convolutional AutoencoderCode1
Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluationCode1
Deep Plug-and-Play Super-Resolution for Arbitrary Blur KernelsCode1
Deep Learning-Based Multiband Signal Fusion for 3-D SAR Super-ResolutionCode1
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual ApproximatorsCode1
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised LearningCode1
Deep Learning-Driven Ultra-High-Definition Image Restoration: A SurveyCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified