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 341–350 of 3874 papers

TitleStatusHype
PatchVSR: Breaking Video Diffusion Resolution Limits with Patch-wise Video Super-Resolution—0
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual ApproximatorsCode1
MIRE: Matched Implicit Neural Representations—0
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution—0
SLVR: Super-Light Visual Reconstruction via Blueprint Controllable Convolutions and Exploring Feature Diversity RepresentationCode0
DORNet: A Degradation Oriented and Regularized Network for Blind Depth Super-Resolution—0
BF-STVSR: B-Splines and Fourier---Best Friends for High Fidelity Spatial-Temporal Video Super-ResolutionCode1
Augmenting Perceptual Super-Resolution via Image Quality Predictors—0
VolFormer: Explore More Comprehensive Cube Interaction for Hyperspectral Image Restoration and BeyondCode1
Hazy Low-Quality Satellite Video Restoration Via Learning Optimal Joint Degradation Patterns and Continuous-Scale Super-Resolution Reconstruction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified