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 121–130 of 3874 papers

TitleStatusHype
Adaptive Super Resolution For One-Shot Talking-Head GenerationCode2
Boosting Flow-based Generative Super-Resolution Models via Learned PriorCode2
XPSR: Cross-modal Priors for Diffusion-based Image Super-ResolutionCode2
Training Generative Image Super-Resolution Models by Wavelet-Domain Losses Enables Better Control of ArtifactsCode2
SeD: Semantic-Aware Discriminator for Image Super-ResolutionCode2
Misalignment-Robust Frequency Distribution Loss for Image TransformationCode2
SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-ResolutionCode2
HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion ModelsCode2
See More Details: Efficient Image Super-Resolution by Experts MiningCode2
Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token DictionaryCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified