Uncertainty-Guided Latent Diffusion Models for Faithful Super Resolution

• URL: http://arxiv.org/abs/2608.25998v1
• Date: Wed, 26 Aug 2026 16:47:08 GMT
• Title: Uncertainty-Guided Latent Diffusion Models for Faithful Super Resolution
• Abstract summary: We introduce UGDiff, a novel diffusion guidance paradigm designed to further improve the perception-distortion balance.<n>In particular, we first estimate the reconstruction uncertainty of the latent features corresponding to a high-fidelity image.<n>This uncertainty is then used to guide the diffusion process to selectively restore high-frequency details in high-uncertainty regions.
• Score: 10.908001570207782
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 the reconstruction uncertainty of the latent features corresponding to a high-fidelity image. This uncertainty is then used to guide the diffusion process to selectively restore high-frequency details in high-uncertainty regions, while preserving fidelity elsewhere. Furthermore, our guidance method adaptively identifies the high-uncertainty regions by considering not only the estimated uncertainty but also the posterior variance of the diffusion sampler at each timestep. This relaxes the reliance on the high-fidelity image in the later stages of sampling, thereby achieving a better perception-distortion balance. Extensive experimental results demonstrate that our method performs favorably against state-of-the-art diffusion-based SR methods.

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