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 591–600 of 3874 papers

TitleStatusHype
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
ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data CharacteristicCode1
Deep Learning-Based Multiband Signal Fusion for 3-D SAR Super-ResolutionCode1
ControlSR: Taming Diffusion Models for Consistent Real-World Image Super ResolutionCode1
Deep Learning-Driven Ultra-High-Definition Image Restoration: A SurveyCode1
Dual Arbitrary Scale Super-Resolution for Multi-Contrast MRICode1
Deep Learning-Based CKM Construction with Image Super-ResolutionCode1
Deep learning architectural designs for super-resolution of noisy imagesCode1
Show:102550
← PrevPage 60 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified