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 181–190 of 3874 papers

TitleStatusHype
Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differencesCode2
Denoising Diffusion Models for Plug-and-Play Image RestorationCode2
AIM 2020 Challenge on Efficient Super-Resolution: Methods and ResultsCode2
Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel FusionCode2
Deep Constrained Least Squares for Blind Image Super-ResolutionCode2
Denoising Diffusion Restoration ModelsCode2
Auto-Encoded Supervision for Perceptual Image Super-ResolutionCode2
DifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-ResolutionCode2
PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-ResolutionCode2
Efficient Mixed Transformer for Single Image Super-ResolutionCode2
Show:102550
← PrevPage 19 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified