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 2501–2510 of 3874 papers

TitleStatusHype
CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priors—0
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: KwaiSR Dataset and Study—0
NUNet: Deep Learning for Non-Uniform Super-Resolution of Turbulent Flows—0
CISRDCNN: Super-resolution of compressed images using deep convolutional neural networks—0
Circumventing the resolution-time tradeoff in Ultrasound Localization Microscopy by Velocity Filtering—0
Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network—0
Ultra-Range Gesture Recognition using a Web-Camera in Human-Robot Interaction—0
You KAN Do It in a Single Shot: Plug-and-Play Methods with Single-Instance Priors—0
ODE-Inspired Network Design for Single Image Super-Resolution—0
ChartEye: A Deep Learning Framework for Chart Information Extraction—0
Show:102550
← PrevPage 251 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified