Uncertainty-Guided Latent Diffusion Models for Faithful Super Resolution
The perception-distortion trade-off poses a fundamental challenge in single-image super-resolution (SR). Although diffusion-based SR methods excel at generating perceptually realistic images, achieving high fidelity remains a key limitation. Recent advances in diffusion-based SR have shown promise in improving fidelity, but these methods often compromise perceptual quality due to their high reliance on a high-fidelity image. To address this, we introduce UGDiff, a novel diffusion guidance paradigm designed to further improve the perception-distortion balance. In particular, we first estimate t
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- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-3.5-large →
“Fuzzy title match (0.73): “Uncertainty-Guided Latent Diffusion Models for Faithful Supe” ≈ “stabilityai/stable-diffusion-3.5-large””
- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-xl-base-1.0 →
“Fuzzy title match (0.73): “Uncertainty-Guided Latent Diffusion Models for Faithful Supe” ≈ “stabilityai/stable-diffusion-xl-base-1.0””
- FuzzySimilar title/name (fuzzy) · 59%CompVis/stable-diffusion-v1-4 →
“Fuzzy title match (0.73): “Uncertainty-Guided Latent Diffusion Models for Faithful Supe” ≈ “CompVis/stable-diffusion-v1-4””
- LinkedLinked via arxiv author · 85%Ren Wang →
“Uncertainty-Guided Latent Diffusion Models for Faithful Super Resolution”
- LinkedLinked via arxiv author · 85%Yung-Yu Chuang →
“Uncertainty-Guided Latent Diffusion Models for Faithful Super Resolution”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”