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 91–100 of 3874 papers

TitleStatusHype
RealViformer: Investigating Attention for Real-World Video Super-ResolutionCode2
Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKVCode2
Arbitrary-Scale Video Super-Resolution with Structural and Textural PriorsCode2
AnySR: Realizing Image Super-Resolution as Any-Scale, Any-ResourceCode2
DiffIR2VR-Zero: Zero-Shot Video Restoration with Diffusion-based Image Restoration ModelsCode2
Binarized Diffusion Model for Image Super-ResolutionCode2
All-In-One Medical Image Restoration via Task-Adaptive RoutingCode2
Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory MatchingCode2
Spectral-Refiner: Accurate Fine-Tuning of Spatiotemporal Fourier Neural Operator for Turbulent FlowsCode2
Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image GenerationCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified