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 161–170 of 3874 papers

TitleStatusHype
Arbitrary-Scale Video Super-Resolution with Structural and Textural PriorsCode2
AnySR: Realizing Image Super-Resolution as Any-Scale, Any-ResourceCode2
Denoising Diffusion Restoration ModelsCode2
Deep Constrained Least Squares for Blind Image Super-ResolutionCode2
Binarized Diffusion Model for Image Super-ResolutionCode2
Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differencesCode2
CDFormer: When Degradation Prediction Embraces Diffusion Model for Blind Image Super-ResolutionCode2
I^2SB: Image-to-Image Schrödinger BridgeCode2
Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-ResolutionCode2
Contourlet Refinement Gate Framework for Thermal Spectrum Distribution Regularized Infrared Image Super-ResolutionCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified