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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3051–3060 of 3874 papers

TitleStatusHype
Semantic-Aware Depth Super-Resolution in Outdoor Scenes—0
Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network—0
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Non-Premixed Combustion on Non-Uniform Meshes and Demonstration of an Accelerated Simulation Workflow—0
VarSR: Variational Super-Resolution Network for Very Low Resolution Images—0
Application of Video-to-Video Translation Networks to Computational Fluid Dynamics—0
SemanticHuman-HD: High-Resolution Semantic Disentangled 3D Human Generation—0
A Plug-and-Play Algorithm for 3D Video Super-Resolution of Single-Photon LiDAR data—0
Semantic Object Prediction and Spatial Sound Super-Resolution with Binaural Sounds—0
Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution—0
Semantic Segmentation Using Super Resolution Technique as Pre-Processing—0
Show:102550
← PrevPage 306 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified