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 2401–2410 of 3874 papers

TitleStatusHype
Impact of deep learning-based image super-resolution on binary signal detection—0
Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual ReconstructionCode1
From General to Specific: Online Updating for Blind Super-Resolution—0
DeepCEL0 for 2D Single Molecule Localization in Fluorescence MicroscopyCode0
Can Super Resolution be used to improve Human Pose Estimation in Low Resolution Scenarios?—0
Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and KernelCode1
On Measuring and Controlling the Spectral Bias of the Deep Image PriorCode1
Blind Image Super-Resolution via Contrastive Representation Learning—0
Text Prior Guided Scene Text Image Super-resolutionCode1
A Mixed-Supervision Multilevel GAN Framework for Image Quality Enhancement—0
Show:102550
← PrevPage 241 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified