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
Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-ResolutionCode2
Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual LossCode2
Exposure Bracketing Is All You Need For A High-Quality ImageCode2
Denoising Diffusion Models for Plug-and-Play Image RestorationCode2
A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-ResolutionCode2
Denoising Diffusion Restoration ModelsCode2
DiffIR2VR-Zero: Zero-Shot Video Restoration with Diffusion-based Image Restoration ModelsCode2
AERO: Audio Super Resolution in the Spectral DomainCode2
Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel FusionCode2
CoSeR: Bridging Image and Language for Cognitive Super-ResolutionCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified