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 1381–1390 of 3874 papers

TitleStatusHype
Multi-BVOC Super-Resolution Exploiting Compounds Inter-Connection—0
A Dive into SAM Prior in Image Restoration—0
Generalized Expectation Maximization Framework for Blind Image Super Resolution—0
Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems—0
Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIsCode1
LeftRefill: Filling Right Canvas based on Left Reference through Generalized Text-to-Image Diffusion ModelCode1
Efficient Mixed Transformer for Single Image Super-ResolutionCode2
Reciprocal Attention Mixing Transformer for Lightweight Image RestorationCode1
mdctGAN: Taming transformer-based GAN for speech super-resolution with Modified DCT spectraCode1
Principal Uncertainty Quantification with Spatial Correlation for Image Restoration ProblemsCode1
Show:102550
← PrevPage 139 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified