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 131–140 of 3874 papers

TitleStatusHype
Distillation-Free One-Step Diffusion for Real-World Image Super-ResolutionCode2
EAMamba: Efficient All-Around Vision State Space Model for Image RestorationCode2
Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-ResolutionCode2
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
All-In-One Medical Image Restoration via Task-Adaptive RoutingCode2
CoSeR: Bridging Image and Language for Cognitive Super-ResolutionCode2
Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel FusionCode2
Denoising Diffusion Models for Plug-and-Play Image RestorationCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified