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 381–390 of 3874 papers

TitleStatusHype
OFTSR: One-Step Flow for Image Super-Resolution with Tunable Fidelity-Realism Trade-offsCode1
A Plug-and-Play Algorithm for 3D Video Super-Resolution of Single-Photon LiDAR data—0
RealOSR: Latent Unfolding Boosting Diffusion-based Real-world Omnidirectional Image Super-ResolutionCode0
Regional climate risk assessment from climate models using probabilistic machine learning—0
FLRONet: Deep Operator Learning for High-Fidelity Fluid Flow Field Reconstruction from Sparse Sensor Measurements—0
Fair Primal Dual Splitting Method for Image Inverse Problems—0
Hero-SR: One-Step Diffusion for Super-Resolution with Human Perception Priors—0
MPSI: Mamba enhancement model for pixel-wise sequential interaction Image Super-Resolution—0
RAP-SR: RestorAtion Prior Enhancement in Diffusion Models for Realistic Image Super-ResolutionCode1
A Progressive Image Restoration Network for High-order Degradation Imaging in Remote Sensing—0
Show:102550
← PrevPage 39 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified