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

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Showing 151–160 of 3874 papers

TitleStatusHype
DVMSR: Distillated Vision Mamba for Efficient Super-ResolutionCode2
EnsIR: An Ensemble Algorithm for Image Restoration via Gaussian Mixture ModelsCode2
Denoising Diffusion Models for Plug-and-Play Image RestorationCode2
All-In-One Medical Image Restoration via Task-Adaptive RoutingCode2
Denoising Diffusion Restoration ModelsCode2
Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differencesCode2
Deep Constrained Least Squares for Blind Image Super-ResolutionCode2
AERO: Audio Super Resolution in the Spectral DomainCode2
AEROMamba: An efficient architecture for audio super-resolution using generative adversarial networks and state space modelsCode2
Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-ResolutionCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified