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Uncertainty-Guided Latent Diffusion Models for Faithful Super Resolution

Ren Wang, Yung-Yu Chuang

arXiv:2608.25998v1cs.CV

TL;DR

Single-image super-resolution faces a perception-distortion trade-off: diffusion models improve realistic detail but make high-fidelity reconstruction difficult. UGDiff estimates latent uncertainty and combines it with sampler posterior variance to guide detail restoration selectively, achieving a better balance than state-of-the-art diffusion-based SR methods.

  • Problem

    Single-image super-resolution must balance perceptual realism with high fidelity, while diffusion-based methods make faithful reconstruction challenging.

  • Method

    UGDiff trains a latent-space uncertainty estimator and uses estimated uncertainty with diffusion-sampler posterior variance to guide selective high-frequency detail restoration.

  • Results

    UGDiff achieves a better perception-distortion balance than state-of-the-art diffusion-based super-resolution methods.

  • Takeaways & Limitations

    Uncertainty-guided diffusion selectively restores details in high-uncertainty regions while preserving fidelity elsewhere.

Abstract

from arXiv · show

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.

1. INTRODUCTION

Single-image super-resolution must balance high fidelity against perceptual realism. UGDiff uses latent uncertainty to guide diffusion toward high-frequency details where reconstruction is uncertain while preserving fidelity elsewhere.

  • Single-image super-resolution recovers a high-resolution image from a low-resolution observation under a degradation model, noise level, and image prior.
  • The perception-distortion trade-off arises because optimizing fidelity and perceptual quality favors different solutions.The paper frames achieving a better balance, rather than optimizing only one metric, as faithful super-resolution.
  • Diffusion-based SR methods generate realistic high-frequency details, but their stochasticity makes high fidelity challenging.Prior methods address fidelity through input guidance, image-gradient weighting, or restoration-guided sampling.
  • UGDiff estimates latent reconstruction uncertainty for a high-fidelity image and uses it to selectively restore high-frequency details in high-uncertainty regions.The method trains a specialized latent-space uncertainty encoder with an L2 loss.
  • UGDiff adaptively combines estimated uncertainty with diffusion-sampler posterior variance to reduce reliance on the high-fidelity image later in sampling.This guidance is intended to improve the perception-distortion balance.
  • The method is reported to achieve the best perception-distortion balance among compared diffusion-based SR methods on RealSR.Figure 1 varies γ for the proposed method at fixed s = 100 and varies s for DiffBIR.

2. RELATED WORK

Related work spans diffusion architectures for super-resolution and uncertainty-based methods for identifying difficult image regions or improving reconstruction robustness.

  • Diffusion-based SR methods differ in training space, conditioning, and prior usage, including image space, residual space, noised low-quality inputs, Stable Diffusion, ControlNet, and restored-image conditioning.
  • Uncertainty research provides frameworks based on entropy or Gaussian likelihood and uses uncertainty to prioritize challenging pixels, frequency components, or kernel estimation.

3. METHOD

UGDiff estimates latent-space uncertainty for a regression-restored image and uses it to adapt diffusion guidance across pixels and timesteps. This selectively restores high-frequency details in uncertain regions while reducing reliance on the restored image later in sampling.

  • 3.1. Preliminaries: Latent diffusion starts from a clean latent z0=E(x0), applies a forward noising process, and reverses it from zT∼N(0,I) to reconstruct an image.For super-resolution, the estimated noise is conditioned on the low-quality input latent E(Ilq).
  • 3.2. Uncertainty Estimation in Latent Space: The uncertainty encoder E′ estimates latent variance for the mean µ=E(Irm) produced by a regression-based restoration model.Training uses a simple L2 loss with Σu=E′(Irm), allowing uncertainty estimation without compromising mean accuracy.
  • 3.3. Uncertainty-Driven Diffusion Guidance: DiffBIR guidance moves sampling toward the restored image Irm, enhancing fidelity in low-textured areas through image-gradient weighting.The guidance uses an image gradient operator G and scale s, with the diffusion mean conditioned on µ=E(Irm).
  • 3.3. Uncertainty-Driven Diffusion Guidance: UGDiff multiplies the estimated uncertainty distribution with the diffusion posterior and uses the resulting maximizer to adapt the guidance direction.The uncertainty is scaled as Σ′u=γΣu, where lower γ favors fidelity and higher uncertainty receives stronger detail restoration.
  • 3.3. Uncertainty-Driven Diffusion Guidance: The posterior weighting is typically large early but decreases faster later, relaxing reliance on µ and improving perceptual quality during later sampling.The weighting behavior is illustrated for different uncertainties under a scaled linear noise scheduler.
  • Experiments: Qualitative evaluations use 4× upsampling on DIV2K-Val and RealSR with s=100 for all methods.The comparisons are presented as upper and bottom panels for DIV2K-Val and RealSR, respectively.

4. EXPERIMENTS

Experiments evaluate UGDiff on synthetic and real 4× super-resolution benchmarks against diffusion-based SR methods and through ablations. The results show improved perceptual quality and perception-distortion balance, while uncertainty-guided sampling changes how guidance parameters affect fidelity and perception.

  • Experiment Settings: Experiments use DIV2K-Val and RealSR test datasets for 4× upsampling from 128 × 128 to 512 × 512.Uncertainty estimation is trained on LSDIR, FFHQ, and generated DIV2K crops.
  • Experiment Settings: The comparison includes StableSR, ResShift, SeeSR, FaithDiff, SUPIR, PiSA-SR, and DiffBIR using official code with default settings.The method is built upon DiffBIR with Stable Diffusion v2.1, ControlNet, and BSRNet.
  • Experiment Settings: PSNR and SSIM measure fidelity, while LPIPS and NIQE measure perceptual quality.PSNR, SSIM, and LPIPS are reference-based; NIQE is non-reference.
  • Ablation Studies: Using BSRNet’s mean estimation yields superior performance across all metrics on DIV2K-Ours.This ablation supports focusing the uncertainty model on accurate uncertainty estimation.
  • Ablation Studies: As guidance scale s increases, DiffBIR improves fidelity but loses perceptual quality, whereas UGDiff exhibits the opposite trend.The authors fix s = 100 because it achieves the optimal balance for γ = 1.0.
  • Comparisons with State-of-the-Arts: At comparable PSNR levels, UGDiff consistently achieves superior perceptual quality against DiffBIR and performs favorably against other state-of-the-art methods, particularly on NIQE.On RealSR, the method achieves the best perception-distortion balance among compared methods.
  • Comparisons with State-of-the-Arts: With comparable PSNR, UGDiff resembles DiffBIR visually, while lower γ removes unnecessary high-frequency details in flat or undesired regions.This behavior supports controlling the perception-distortion trade-off through uncertainty-guided sampling.

5. CONCLUSION

The paper presents latent-space uncertainty estimation for guiding diffusion sampling in faithful super-resolution. Experiments report a better perception-distortion balance than state-of-the-art methods, with future work targeting efficiency and broader applicability.

  • 5. CONCLUSION: The method trains a latent-space uncertainty estimation model and uses its uncertainty to guide the diffusion process.The paper identifies single-step distillation and extension to broader task ranges as possible future directions.
  • 5. CONCLUSION: Experimental results demonstrate a better perception-distortion balance than state-of-the-art methods.
  • 5. CONCLUSION: Future work may distill the diffusion process toward single-step sampling or extend the method to a broader range of tasks.

A. ABLATION STUDY ON PARAMETERIZATION

The parameterization ablation compares predicting σu with predicting σ2u for the uncertainty model. The σu parameterization performs better on most metrics and is selected for the final solution.

  • A. ABLATION STUDY ON PARAMETERIZATION: The ablation compares whether the uncertainty model outputs σu or σ2u on DIV2K-Ours.The experiments use s = 100 and γ = 0.1.
  • A. ABLATION STUDY ON PARAMETERIZATION: The σu parameterization performs better on most metrics and is chosen as the final solution.The selected uncertainty is defined as Σu = E′(Irm) ⊙ E′(Irm).

B. VISUAL COMPARISON ON UNCERTAINTY SCALE

The uncertainty scale γ controls the amount of high-frequency detail in UGDiff outputs. Higher γ favors richer details, whereas lower γ avoids unnecessary details and produces results closer to BSRNet.

  • B. VISUAL COMPARISON ON UNCERTAINTY SCALE: Higher γ is preferred when richer high-frequency details are desired.The comparison is shown for DIV2K-Val and RealSR with s = 100.
  • B. VISUAL COMPARISON ON UNCERTAINTY SCALE: Lower γ avoids unnecessary details and makes the result look more like the restoration model BSRNet.

C. IMPACT OF GUIDANCE ON THE SAMPLING PROCESS

The guidance suppresses unnecessary textures in low-uncertainty regions while preserving perceptual quality, improving the perception-distortion balance. Qualitative comparisons further show that setting γ = 0.1 removes unnecessary texture details relative to compared methods.

  • Sampling-process analysis: Guidance suppresses unnecessary textures in low-uncertainty regions, resulting in higher PSNR.The comparison visualizes six of 50 sampling steps.
  • Sampling-process analysis: The NIQE curve with guidance is nearly identical to the baseline, indicating preserved perceptual quality.The adaptive mechanism relaxes reliance on µ at later sampling steps.
  • Sampling-process analysis: The sampling-process comparison uses s = 100 and γ = 0.1 for the proposed guidance.
  • Qualitative comparisons: With γ = 0.1, the method removes unnecessary texture details and achieves a better perception-distortion balance than compared methods.With γ = 0.5, it performs similarly to DiffBIR on DRealSR.

E. PROOFS

The supplied passages indicate that the proofs consider a deterministic point-wise term when zgt and µ are given. The accompanying figures are qualitative comparisons of 4× upsampling on DRealSR and DIV2K-Val.

  • Proofs: When zgt and µ are given, the point-wise term in Lnll is deterministic.The passage states that the gradient can consequently be set to zero.
  • Proofs: The proof passage states that the gradient can be set to zero under the stated condition.
  • Qualitative comparisons: Figure 7 presents a qualitative comparison of 4× upsampling on DRealSR, with s = 100 for the methods shown.
  • Qualitative comparisons: Figure 8 presents a qualitative comparison of 4× upsampling on DIV2K-Val, with s = 100 for the methods shown.
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