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 1011–1020 of 3874 papers

TitleStatusHype
TTRD3: Texture Transfer Residual Denoising Dual Diffusion Model for Remote Sensing Image Super-ResolutionCode1
End-to-End Learning for Joint Image Demosaicing, Denoising and Super-ResolutionCode1
Diffusion Prior Interpolation for Flexibility Real-World Face Super-ResolutionCode1
Tunable Convolutions with Parametric Multi-Loss OptimizationCode1
Enhanced Quadratic Video InterpolationCode1
DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-ResolutionCode1
Unfolded Deep Kernel Estimation for Blind Image Super-resolutionCode1
Unfolding Once is Enough: A Deployment-Friendly Transformer Unit for Super-ResolutionCode1
Boosting Single Image Super-Resolution via Partial Channel ShiftingCode1
Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain ImagingCode1
Show:102550
← PrevPage 102 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified