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.
Figures & tables
Figure 1: Perception-Distortion trade-off of diffusion-based SR methods on RealSR. The DiffBIR curve corresponds to various values of s , while ours varies γ at a fixed s=100 .
Figure 2: Overview of our method. Given a high-fidelity image Irm restored from the low-quality image Ilq by a regression-based restoration model R , our uncertainty estimation model E′ produces a variance map Σu specifically for μ=E(Irm) . During the reverse process of a pretrained latent diffusion model, we rectify μθ(zt,z^0∣t) , i.e . , the mean of the posterior distribution p(zt−1∣zt,z^0∣t) , at each timestep t . Our guidance aims to push μθ(zt,z^0∣t) to the direction of μ according to the uncertainty at each pixel. Repeating this process, we get the final restored image Ihq at timestep 0 , which is expected to have plentiful details in textured regions while remaining robust against hallucinations and artifacts elsewhere.
Figure 3: Comparison of 4× upsampling on DIV2K-Val (upper) and RealSR (bottom). We set s=100 for all of our methods.
Dataset
Method
PSNR ↑
SSIM ↑
LPIPS ↓
NIQE ↓
DIV2K-Val
StableSR [ 5 ]
21.6100
0.5267
0.3113
4.7581
ResShift [ 4 ]
22.6640
0.5884
0.3077
6.9142
SeeSR [ 17 ]
21.9703
0.5592
0.3194
4.8097
FaithDiff [ 12 ]
21.7965
0.5348
0.3118
4.9149
SUPIR [ 9 ]
21.4792
0.5005
0.3625
6.3373
PiSA-SR [ 11 ]
22.2189
0.5624
0.2823
4.5577
Table 1: Quantitative comparisons of 4× upsampling. Underline indicates superior performance between DiffBIR and ours at comparable PSNR levels. Red and blue respectively indicate the best and the second best performance among all methods. We set s=100 for all of our methods.
Figure 4: Relative ratio of weighting over time under different uncertainties σu2 . The ratio is defined by ρ=σt2/(αˉt−1σu2) . Note that the curves are depicted using the scaled linear noise scheduler with β1=8.5×10−4 and βT=1.2×10−2 .
Irm
PSNR ↑
SSIM ↑
LPIPS ↓
NIQE ↓
BSRNet(Ilq)
20.2307
0.4222
0.4388
4.6974
D(Enll(Ilq))
19.5111
0.3844
0.4854
5.6106
Table 2: Ablation study on mean estimation. Bold indicates superior performance. We conduct the experiments using DiffBIR [ 8 ] without guidance ( s=0 ).
s
γ
PSNR ↑
SSIM ↑
LPIPS ↓
NIQE ↓
1
1.0
20.2278
0.4222
0.4387
4.6801
10
1.0
20.2040
0.4219
0.4371
4.5573
100
1.0
19.8139
0.4113
0.4271
3.7287
200
1.0
18.9496
0.3779
0.4628
4.0655
100
1.0
19.8139
0.4113
0.4271
3.7287
100
0.5
20.2100
0.4340
0.4185
3.8541
Table 3: Ablation study on guidance parameters. Bold indicates the best performance.
Appendix figures & tables5 assets
Supplementary material from the paper’s appendix.
Appendix
Param.
PSNR ↑
SSIM ↑
LPIPS ↓
NIQE ↓
σu2
20.7987
0.4676
0.4118
4.3496
σu
20.9637
0.4780
0.4095
4.5425
Appendix
Table 4: Ablation study on parameterization. Bold indicates the better performance. We set s=100 and γ=0.1 for the experiments.
Figure 5: Visual comparison under different uncertainty scales on DIV2K-Val (upper) and RealSR (bottom). We set s=100 for all of our methods.
Figure 6: Visualization and analysis of the sampling process with and w/o our guidance. We set s=100 and γ=0.1 for the guidance.
Figure 7: Qualitative comparison of 4× upsampling on DRealSR. We set s=100 for all of our methods.
Figure 8: Qualitative comparison of 4× upsampling on DIV2K-Val. We set s=100 for all of our methods.
Image super-resolution (SR) aims to reconstruct high resolution images with both high perceptual quality and low distortion, but is fundamentally limited by the perception-distortion trade-off. GAN-based SR methods reduce distortion but still struggle with realistic fine-grained textures, whereas diffusion-based approaches synthesize rich details but often deviate from the input, hallucinating structures and degrading fidelity. This tension raises a key challenge: how to exploit the powerful generative priors of diffusion models without sacrificing fidelity. To address this, we propose SpaSemSR, a spatial-semantic guided diffusion framework with two complementary guidances. First, spatial-grounded textual guidance integrates object-level spatial cues with semantic prompts, aligning textual and visual structures to reduce distortion. Second, semantic-enhanced visual guidance with a multi-encoder design and semantic degradation constraints unifies multimodal semantic priors, improving perceptual realism under severe degradations. These complementary guidances are adaptively fused into the diffusion process via spatial-semantic attention, suppressing distortion and hallucination while retaining the strengths of diffusion models. Extensive experiments on multiple benchmarks show that SpaSemSR achieves a superior perception-distortion balance, producing both realistic and faithful restorations.
Dan Wang, Haiyan Sun, Shan Du +4
University of California, San Diego · University of North Texas · University of British Columbia +1
The effectiveness of super resolution (SR) models hinges on their ability to recover high frequency structure without introducing artifacts. Diffusion based approaches have recently advanced the state of the art in SR. However, most diffusion based SR pipelines operate purely in the spatial domain, which may yield high frequency details that are not well supported by the underlying low resolution evidence. On the other hand, unlike supervised SR models that may inject dataset specific textures, single image SR relies primarily on internal image statistics and can therefore be less prone to dataset-driven hallucinations; nevertheless, ambiguity in the LR observation can still lead to inconsistent high frequency details. To tackle this problem, we introduce BATDiff, an unsupervised Bivariate A trous Wavelet Diffusion model designed to provide structured cross scale guidance during the generative process. BATDiff employs an a Trous wavelet transform that constructs an undecimated multiscale representation in which high frequency components are progressively revealed while the full spatial resolution is preserved. As the core inference mechanism, BATDiff includes a bivariate cross scale module that models parent child dependencies between adjacent scales. It improves high frequency coherence and reduces mismatch artifacts in diffusion based SR. Experiments on standard benchmarks demonstrate that BATDiff produces sharper and more structurally consistent reconstructions than existing diffusion and non diffusion baselines, achieving improvements in fidelity and perceptual quality.
Diffusion-based approaches have recently driven remarkable progress in real-world image super-resolution (SR). However, existing methods still struggle to simultaneously preserve fine details and ensure high-fidelity reconstruction, often resulting in suboptimal visual quality. In this paper, we propose FiDeSR, a high-fidelity and detail-preserving one-step diffusion super-resolution framework. During training, we introduce a detail-aware weighting strategy that adaptively emphasizes regions where the model exhibits higher prediction errors. During inference, low- and high-frequency adaptive enhancers further refine the reconstruction without requiring model retraining, enabling flexible enhancement control. To further improve the reconstruction accuracy, FiDeSR incorporates a residual-in-residual noise refinement, which corrects prediction errors in the diffusion noise and enhances fine detail recovery. FiDeSR achieves superior real-world SR performance compared to existing diffusion-based methods, producing outputs with both high perceptual quality and faithful content restoration. The source code will be released at: https://github.com/Ar0Kim/FiDeSR.
Aro Kim, Myeongjin Jang, Chaewon Moon +3
Kyungpook National University · Korea Electronics Technology Institute