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REVIEW 3 major objections 7 minor 54 references

Hero-SR: One-Step Diffusion for Super-Resolution with Human Perception Priors

T0 review · 3 major / 7 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Hero-SR is a one-step diffusion super-resolution model that adaptively picks a per-image diffusion step and applies CLIP text-image supervision to achieve state-of-the-art perceptual quality on real-world benchmarks.

desk verdict Solid incremental one-step SR paper whose adaptive time-step idea is worth knowing, but the human-perception claim needs human data before it can be taken at face value. read the letter →

arxiv 2412.07152 v1 pith:CECSOJRJ submitted 2024-12-10 cs.CV

Jiangang Wang , Qingnan Fan , Qi Zhang , Haigen Liu , Yuhang Yu , Jinwei Chen , Wenqi Ren This is my paper · ORCID
classification cs.CV
keywords real-worldsuper-resolutionone-stepdiffusiondynamictime-stepselectionCLIPmultimodalsupervisionperceptualqualityno-referenceimageassessmentsemanticconsistencyrestoration
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper aims to show that a single diffusion step can produce real-world super-resolved images that meet human perceptual standards, provided the model adaptively selects its starting noise level per input image and supervises the output with CLIP-based multimodal guidance. On the DIV2K, DRealSR, and RealSR benchmarks, Hero-SR reports the best or second-best scores among compared one-step and multi-step diffusion methods across no-reference perceptual metrics such as MUSIQ, HyperIQA, TOPIQ, TRES, ARNIQA, and Q-Align. The authors argue this is significant because existing one-step methods fix a single starting time step for every image, ignoring the wide variation in degradation and structural complexity across real-world inputs, and because semantic consistency and perceptual naturalness are usually under-supervised. If the claim holds, perceptually aligned super-resolution can be achieved in a single diffusion step, lowering inference cost while matching or exceeding slower multi-step approaches.

What carries the argument

Two modules carry the argument. The Dynamic Time-Step Module (DTSM) maps a low-resolution image to a noise-level index via convolution, residual blocks, an MLP, and Gumbel-Softmax, enabling a differentiable per-image choice of denoising start point. The Open-World Multi-modality Supervision (OWMS) combines Text-Domain Perceptual Alignment Loss (TD-PAL), which uses CLIP to score cosine similarity between the generated image and positive/negative text prompt pairs for six perceptual attributes, and Image-Domain Semantic Alignment Loss (ID-SAL), which aligns the CLIP image embedding of the output with that of the ground truth. Together they define the total loss alongside MSE and LPIPS terms, with SD-Turbo as the base diffusion model fine-tuned by LoRA.

What would settle it

Run a paired human preference study on the same test sets (DIV2K, DRealSR, RealSR) comparing Hero-SR output against the closest baselines such as SeeSR and OSEDiff; if human raters do not prefer Hero-SR at rates significantly above chance, the perceptual claim is falsified. Alternatively, recompute the no-reference metrics over multiple training runs and check whether the reported gaps (e.g., the 7.0% TOPIQ improvement) exceed run-to-run variance.

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Extended reading notes

Core claim

The central claim is that human-perceptual alignment in one-step real-world super-resolution is achievable by jointly (1) dynamically selecting the diffusion time step $t^*$ according to the input's degradation and complexity, via a convolutional feature extractor and Gumbel-Softmax sampling, and (2) supervising the output with CLIP in both text and image domains: text prompts representing perceptual attributes (quality, sharpness, edge clarity, resolution, noise, clarity) pull the image toward positive attributes and away from negative ones, while image-domain alignment with the ground truth enforces semantic consistency. On DIV2K, DRealSR, and RealSR test sets, the model reports the best or second-best scores on no-reference metrics such as MUSIQ, HyperIQA, TOPIQ, TRES, ARNIQA, and Q-Align among compared one-step and multi-step methods, with a single diffusion step.

Load-bearing premise

The paper's headline conclusion rests on the assumption that the no-reference quality metrics it optimizes for and reports (MUSIQ, HyperIQA, TOPIQ, TRES, ARNIQA, Q-Align) are faithful proxies for human perception; if those metrics disagree with human preference, or if the reported single-run differences are within metric noise, the state-of-the-art claim loses its support.

Editorial extensions

If this is right

  • Per-image time-step selection can be applied to other one-step diffusion restoration tasks, since degradation level varies across images.
  • The reported results imply that a single diffusion step can match or exceed multi-step diffusion methods on no-reference perceptual metrics, making high-quality super-resolution more computationally accessible.
  • Incorporating CLIP-based text supervision in the training objective provides a way to inject human perceptual attributes without collecting human preference labels.
  • The combination of TD-PAL and ID-SAL suggests that both text-defined attributes and image-level semantic consistency contribute to perceptual quality, and ablations indicate each module is needed for top scores.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, the DTSM design implies that the fixed start-step convention in one-step diffusion distillation may itself be a bottleneck; adaptivity could generalize to text-to-image or image translation with variable difficulty.
  • Our extension: the unstated candidate set $S$ and Gumbel-Softmax temperature in Eq.~4 are reproducibility-critical; we infer that sweeping them would change perceptual scores, so the paper's claim is only fully testable once those hyperparameters are disclosed.
  • Our extension: if no-reference IQA metrics are the optimization target, a risk is that the model over-fits those particular metrics rather than human perception; a human study would separate these cases.
  • We also infer that the framework may be extended by replacing the fixed six perceptual attribute prompts with an open-vocabulary or learned prompt set, potentially improving adaptation to new degradation types.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 7 minor

Summary. The paper proposes Hero-SR, a one-step diffusion-based super-resolution framework with two new components: a Dynamic Time-Step Module (DTSM) that selects a per-image starting diffusion step, and Open-World Multi-modality Supervision (OWMS) that adds CLIP-based text-domain and image-domain losses. The authors report experiments on DIV2K-val, DRealSR, and RealSR, comparing with one-step and multi-step diffusion SR baselines, and claim state-of-the-art performance on no-reference perceptual metrics such as MUSIQ, HyperIQA, TOPIQ, TRES, ARNIQA, and Q-Align. The paper includes ablations for DTSM, ID-SAL, TD-PAL, and individual perceptual attribute prompts, and states that code will be released.

Significance. If the perceptual-quality claim is substantiated, the paper makes a useful contribution: DTSM is a novel alternative to fixed-step one-step diffusion SR, and OWMS gives a simple, potentially reproducible recipe for injecting human-perceptual priors via frozen CLIP encoders. The module-level and attribute-level ablations are valuable, and the promise of public code is a strength. However, the current evaluation does not yet establish the central claim of superior alignment with human perception, and the DTSM specification is incomplete. The result is therefore promising but not yet fully supported.

major comments (3)
  1. [Section 3.2, Eq. (4)] The text makes blanket quantitative claims that are contradicted by the paper's own tables. It states that Hero-SR 'outperforms other methods across all datasets for no-reference perceptual metrics' and 'achieves leading results in full-reference metrics, surpassing other methods in PSNR, SSIM, and LPIPS.' Table 3 shows SeeSR achieves higher HyperIQA and TRES on DRealSR (0.6642 vs 0.6434 and 84.7264 vs 83.5888, respectively) and higher TOPIQ on RealSR (0.6891 vs 0.6881). Table 2 and Table 3 also show SinSR and ResShift exceeding Hero-SR on PSNR on some datasets (e.g., DIV2K PSNR 24.4111 vs 24.3663 for SinSR, RealSR PSNR 26.5344 vs 25.8271 for ResShift). These overstatements need to be corrected, and the claims should be aligned with the actual tables.
  2. [Section 3.2, Eq. (4)] The DTSM is underspecified and therefore not reproducible. Eq. (4) defines t* = Gumbel-Softmax(v, S), but the candidate set S is never described—its size, elements, and spacing are all omitted. The Gumbel-Softmax temperature is also never reported, and it is unclear whether a discrete or continuous relaxation is used at inference. Furthermore, the paper does not explain how the selected t* is fed to the U-Net (e.g., as a discrete step index, a time embedding, or a vector) and whether zLR is first noised to that step when t* is not the maximal step. Consequently, the DTSM ablation in Table 5 is difficult to interpret because the fixed-step baseline used in Variant-1 is not specified.
  3. [Section 4.2.1 and Section 4.3] The central claim that Hero-SR better aligns with human perception is not yet established by the reported evidence. All perceptual comparisons rely on single-run means of learned no-reference IQA metrics, with no error bars, significance tests, or human-preference study. This is particularly concerning because TD-PAL explicitly optimizes CLIP similarity between the generated image and positive perceptual prompts, and the evaluation metrics (MUSIQ, HyperIQA, TOPIQ, TRES, ARNIQA, Q-Align) are themselves learned models that may use CLIP-like features; the evaluation could therefore partly reward the training objective rather than independently confirm perceptual superiority. Some reported differences are very small (e.g., RealSR MUSIQ 70.0254 vs 69.9864 in Table 2), so without confidence intervals or a human study, these differences cannot be distinguished from metric noise.
minor comments (7)
  1. [Eq. (9) and Eq. (13)] The loss is named LTD-SAL in Eq. (9) but is referred to as TD-PAL in the text and as LTD-PAL in Eq. (13); please unify the notation.
  2. [Section 4.1] The compared methods list contains the typo 'ResShit' instead of 'ResShift'.
  3. [Reference [1]] The ARNIQA reference is listed as CVPR 2024, but the paper actually appeared at WACV 2024.
  4. [Throughout] There are several typos: 'Fighure 2' in Section 3.1, 'sematic' in Section 3.3.2, and 'GAN-base' in Appendix A.
  5. [Table 4 and Section 4.3] Table 4 is titled 'perceptual attributions' while the text uses 'perceptual attributes'; please use consistent terminology.
  6. [Section 1, contributions] The claim 'we are the first to incorporate multimodal models into the training of Real-SR tasks' is too strong given prior semantics-aware Real-SR methods such as SeeSR, PASD, and SUPIR that use textual or CLIP-based guidance; please qualify this contribution.
  7. [Section 5] The limitation paragraph only mentions VAE reconstruction capacity; it should also acknowledge the lack of a human-preference evaluation for the perceptual claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: Hero-SR's training losses and evaluation metrics are distinct external signals; the central SOTA claim is not forced by construction or by self-citation.

full rationale

The derivation chain is self-contained. Hero-SR's modules are trained with the composite loss in Eq. (13), combining MSE, LPIPS, CLIP-based text-image alignment (TD-PAL), and CLIP-based image-image alignment (ID-SAL); these are training inputs, not predicted outputs. The no-reference evaluation metrics (MUSIQ, HyperIQA, TOPIQ, TRES, ARNIQA, Q-Align) are external, separately trained IQA models rather than renames of the CLIP similarity objective, so evaluating with them does not reduce to the TD-PAL training signal. DTSM's time-step t* is learned on training pairs via Gumbel-Softmax and assessed on held-out StableSR/RealSR/DRealSR benchmarks; it is not fitted to the test-set metrics, so the reported gains are genuine holdout measurements rather than fitted-input predictions. The only author-overlapping citation is S3Diff [51], which includes co-author Wenqi Ren; it is used as a comparison baseline and to motivate the fixed-time-step limitation, not as the load-bearing justification for Hero-SR's claims. Unspecified implementation details (candidate set S and Gumbel temperature in Eq. (4)) and reliance on no-reference IQA proxies are reproducibility and external-validity concerns, not circularity. No step reduces by construction to its own input.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The paper's contribution is primarily empirical. It introduces no new physical or mathematical entities. It relies on a set of hand-chosen hyperparameters and on the validity of two learned priors (SD-Turbo and CLIP) plus the Real-ESRGAN degradation model. The DTSM design contains unspecified free choices (S, temperature) that directly affect the claimed improvement.

free parameters (4)
  • Loss weights lambda1..lambda4 = 2, 5, 1, 0.5
    Hand-set in Section 4.1 Implementation Details; no sensitivity analysis is shown.
  • Number and choice of perceptual attribute prompt pairs = 6 pairs from Table 1
    Hand-selected attributes (quality, sharpness, edge clarity, resolution, noise, clarity); ablation shows each helps but no principled selection.
  • Candidate time-step subset S = unspecified
    Defined in Section 3.2 as a subset of {0,...,999} but never enumerated; the actual choices determine DTSM behavior and are not reported.
  • Gumbel-Softmax temperature = unspecified
    Required for Eq. (4) but not reported; temperature controls how hard or soft the time-step selection is.
assumptions (5)
  • domain assumption Pre-trained Stable Diffusion or SD-Turbo provides a strong natural-image prior that can be repurposed for SR.
    The whole framework is built on SD-Turbo with LoRA (Sections 3.1 and 4.1); this prior is assumed to be transferable.
  • domain assumption CLIP embeddings capture semantic content and coarse perceptual quality across text and images.
    OWMS relies on frozen CLIP encoders (Section 3.3); if CLIP does not align with human perception, both losses are misplaced.
  • domain assumption The Real-ESRGAN degradation pipeline produces LR/HR pairs representative of real-world degradations.
    Training pairs are generated with this pipeline (Section 4.1); the transfer to RealSR/DRealSR is assumed to hold.
  • domain assumption No-reference IQA metrics (MUSIQ, HyperIQA, TOPIQ, TRES, ARNIQA, Q-Align) are valid proxies for human perceptual preference.
    The SOTA claim is built on these metrics (Section 4.2.1, Tables 2 and 3); no human study is included.
  • ad hoc to paper The candidate time-step set S is fixed and contains the optimal step for each input.
    DTSM selects among S, but S is never specified; the success of the module depends on an unstated design choice.

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Cite this review

Pith. "Pith review of Hero-SR: One-Step Diffusion for Super-Resolution with Human Perception Priors." pith.science (2026). https://pith.science/paper/CECSOJRJ

@misc{pith2026241207152,
  author       = {Pith},
  title        = {Pith review of: Hero-SR: One-Step Diffusion for Super-Resolution with Human Perception Priors},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CECSOJRJ}},
  note         = {Machine review of arXiv:2412.07152}
}
read the original abstract

Owing to the robust priors of diffusion models, recent approaches have shown promise in addressing real-world super-resolution (Real-SR). However, achieving semantic consistency and perceptual naturalness to meet human perception demands remains difficult, especially under conditions of heavy degradation and varied input complexities. To tackle this, we propose Hero-SR, a one-step diffusion-based SR framework explicitly designed with human perception priors. Hero-SR consists of two novel modules: the Dynamic Time-Step Module (DTSM), which adaptively selects optimal diffusion steps for flexibly meeting human perceptual standards, and the Open-World Multi-modality Supervision (OWMS), which integrates guidance from both image and text domains through CLIP to improve semantic consistency and perceptual naturalness. Through these modules, Hero-SR generates high-resolution images that not only preserve intricate details but also reflect human perceptual preferences. Extensive experiments validate that Hero-SR achieves state-of-the-art performance in Real-SR. The code will be publicly available upon paper acceptance.

Figures

Figures reproduced from arXiv: 2412.07152 by the authors.

Figure 1
Figure 1. Performance and Visual Comparison. (1) Performance Comparison: Compared to one-step and multi-step methods, Hero-SR [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Training framework of Hero-SR. Hero-SR incorporates a Dynamic Time-step Module to adaptively determine the optimal time [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The time-step selection process of DTSM. Previous one [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison with one-step and multi-step methods. ‘S’ indicates the number of diffusion steps. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Qualitative comparison with GAN-base methods. [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: Qualitative comparison with one-step and multi-step methods. ‘S’ indicates the number of diffusion steps. [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: Qualitative comparison with one-step and multi-step methods. ‘S’ indicates the number of diffusion steps. [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]
Figure 8
Figure 8. Figure 8: Qualitative comparison with one-step and multi-step methods. ‘S’ indicates the number of diffusion steps. [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 9
Figure 9. Figure 9: Qualitative comparison with one-step and multi-step methods. ‘S’ indicates the number of diffusion steps. [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]

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Reference graph

Works this paper leans on

54 extracted references · 50 canonical work pages

  1. [1]

    ARNIQA: learning distortion manifold for image quality assessment

    Lorenzo Agnolucci, Leonardo Galteri, Marco Bertini, and Alberto Del Bimbo. ARNIQA: learning distortion manifold for image quality assessment. In IEEE/CVF Winter Confer- ence on Applications of Computer Vision, CVPR 2024, pages 188–197, 2024. 6

    work page 2024

  2. [2]

    NTIRE 2017 chal- lenge on single image super-resolution: Dataset and study

    Eirikur Agustsson and Radu Timofte. NTIRE 2017 chal- lenge on single image super-resolution: Dataset and study. In IEEE Conference on Computer Vision and Pattern Recog- nition Workshops. CVPRW 2017, pages 1122–1131, 2017. 5, 11

    work page 2017

  3. [3]

    Blended diffusion for text-driven editing of natural images

    Omri Avrahami, Dani Lischinski, and Ohad Fried. Blended diffusion for text-driven editing of natural images. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, pages 18187–18197, 2022. 3

    work page 2022

  4. [4]

    Toward real-world single image super-resolution: A new benchmark and a new model

    Jianrui Cai, Hui Zeng, Hongwei Yong, Zisheng Cao, and Lei Zhang. Toward real-world single image super-resolution: A new benchmark and a new model. In IEEE/CVF Interna- tional Conference on Computer Vision, ICCV 2019 , pages 3086–3095, 2019. 2, 4, 5, 11

    work page 2019

  5. [5]

    Camera lens super-resolution

    Chang Chen, Zhiwei Xiong, Xinmei Tian, Zheng-Jun Zha, and Feng Wu. Camera lens super-resolution. In IEEE Con- ference on Computer Vision and Pattern Recognition, CVPR 2019, pages 1652–1660, 2019. 1

    work page 2019

  6. [6]

    Real-world blind super-resolution via feature matching with implicit high- resolution priors

    Chaofeng Chen, Xinyu Shi, Yipeng Qin, Xiaoming Li, Xi- aoguang Han, Tao Yang, and Shihui Guo. Real-world blind super-resolution via feature matching with implicit high- resolution priors. In 30th ACM International Conference on Multimedia, pages 1329–1338, 2022. 6, 11

    work page 2022

  7. [7]

    TOPIQ: A top-down approach from semantics to distortions for image quality assessment

    Chaofeng Chen, Jiadi Mo, Jingwen Hou, Haoning Wu, Liang Liao, Wenxiu Sun, Qiong Yan, and Weisi Lin. TOPIQ: A top-down approach from semantics to distortions for image quality assessment. IEEE Trans. Image Process., 33:2404– 2418, 2024. 6

    work page 2024

  8. [8]

    Stable long-term recurrent video super-resolution

    Benjamin Naoto Chiche, Arnaud Woiselle, Joana Frontera- Pons, and Jean-Luc Starck. Stable long-term recurrent video super-resolution. In IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, CVPR 2022 , pages 827–836,

    work page 2022

Show all 54 references
  1. [9]

    Come-closer-diffuse-faster: Accelerating conditional diffu- sion models for inverse problems through stochastic contrac- tion

    Hyungjin Chung, Byeongsu Sim, and Jong Chul Ye. Come-closer-diffuse-faster: Accelerating conditional diffu- sion models for inverse problems through stochastic contrac- tion. In IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, CVPR 2022, pages 12403–12412, 2022. 3

    2022

  2. [10]

    Learning a deep convolutional network for image super-resolution

    Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. Learning a deep convolutional network for image super-resolution. In Proceedings of European Conference on Computer Vision, Part IV, pages 184–199, 2014. 2

    2014

  3. [11]

    Acceler- ating the super-resolution convolutional neural network

    Chao Dong, Chen Change Loy, and Xiaoou Tang. Acceler- ating the super-resolution convolutional neural network. In Proceedings of European Conference on Computer Vision, Part II, pages 391–407, 2016. 2

    2016

  4. [12]

    Alireza Golestaneh, Saba Dadsetan, and Kris M

    S. Alireza Golestaneh, Saba Dadsetan, and Kris M. Kitani. No-reference image quality assessment via transformers, rel- ative ranking, and self-consistency. In IEEE/CVF Win- ter Conference on Applications of Computer Vision, CVPR 2022, pages 3989–3999, 2022. 6

    2022

  5. [13]

    Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C

    Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. Generative adversarial nets. In Ad- vances in Neural Information Processing Systems , pages 2672–2680, 2014. 2

    2014

  6. [14]

    Blind super-resolution with iterative kernel correction

    Jinjin Gu, Hannan Lu, Wangmeng Zuo, and Chao Dong. Blind super-resolution with iterative kernel correction. In IEEE Conference on Computer Vision and Pattern Recogni- tion, CVPR 2019, pages 1604–1613, 2019. 2

    2019

  7. [15]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In IEEE Con- ference on Computer Vision and Pattern Recognition, CVPR 2016, pages 770–778, 2016. 4

    2016

  8. [16]

    Denoising dif- fusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising dif- fusion probabilistic models. In Advances in Neural Informa- tion Processing Systems, 2020. 2, 3, 4

    2020

  9. [17]

    Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

    Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. In Tenth International Conference on Learning Representa- tions, ICLR 2022, 2022. 4, 6

    2022

  10. [18]

    Categorical repa- rameterization with gumbel-softmax

    Eric Jang, Shixiang Gu, and Ben Poole. Categorical repa- rameterization with gumbel-softmax. In 5th International Conference on Learning Representations, ICLR 2017, 2017. 4

    2017

  11. [19]

    MUSIQ: multi-scale image quality transformer

    Junjie Ke, Qifei Wang, Yilin Wang, Peyman Milanfar, and Feng Yang. MUSIQ: multi-scale image quality transformer. In IEEE/CVF International Conference on Computer Vision, CVPR 2021, pages 5128–5137, 2021. 6, 11

    2021

  12. [20]

    Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, and Wen- zhe Shi

    Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew P. Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, and Wen- zhe Shi. Photo-realistic single image super-resolution us- ing a generative adversarial network. In IEEE Conf...

    2017

  13. [21]

    LSDIR: A large scale dataset for image restoration

    Yawei Li, Kai Zhang, Jingyun Liang, Jiezhang Cao, Ce Liu, Rui Gong, Yulun Zhang, Hao Tang, Yun Liu, Denis Demandolx, Rakesh Ranjan, Radu Timofte, and Luc Van Gool. LSDIR: A large scale dataset for image restoration. In IEEE/CVF Conference on Computer Vision and Pattern Recogni...

    2023

  14. [22]

    Swinir: Image restoration using swin transformer

    Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. Swinir: Image restoration using swin transformer. In IEEE/CVF International Confer- ence on Computer Vision Workshops, ICCVW 2021 , pages 1833–1844, 2021. 2, 6, 11

    2021

  15. [23]

    Details or artifacts: A locally discriminative learning approach to realistic im- age super-resolution

    Jie Liang, Hui Zeng, and Lei Zhang. Details or artifacts: A locally discriminative learning approach to realistic im- age super-resolution. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 , pages 5647– 5656, 2022. 2

    2022

  16. [24]

    Diff- bir: Towards blind image restoration with generative diffu- sion prior

    Xinqi Lin, Jingwen He, Ziyan Chen, Zhaoyang Lyu, Bo Dai, Fanghua Yu, Wanli Ouyang, Yu Qiao, and Chao Dong. Diff- bir: Towards blind image restoration with generative diffu- sion prior. arXiv preprint arXiv:2308.15070, 2023. 2, 3, 5

    2023 arXiv

  17. [25]

    Decoupled weight decay regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In 7th International Conference on Learning Representations, ICLR 2019, 2019. 6

    2019

  18. [26]

    Repaint: Inpainting using denoising diffusion probabilistic models

    Andreas Lugmayr, Martin Danelljan, Andr´es Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. Repaint: Inpainting using denoising diffusion probabilistic models. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, pages 11451–11461, 2022. 3

    2022

  19. [27]

    Gustafsson, Zheng Zhao, Jens Sj¨olund, and Thomas B

    Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sj¨olund, and Thomas B. Sch ¨on. Image restoration with mean-reverting stochastic differential equations. In Interna- tional Conference on Machine Learning, ICLR 2023 , pages 23045–23066, 2023. 4

    2023

  20. [28]

    Sdedit: Guided image synthesis and editing with stochastic differential equa- tions

    Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jia- jun Wu, Jun-Yan Zhu, and Stefano Ermon. Sdedit: Guided image synthesis and editing with stochastic differential equa- tions. In Tenth International Conference on Learning Repre- sentations, ICLR 2022, 2022. 3

    2022

  21. [29]

    One-step image translation with text-to-image models

    Gaurav Parmar, Taesung Park, Srinivasa Narasimhan, and Jun-Yan Zhu. One-step image translation with text-to-image models. ArXiv preprint, abs/2403.12036, 2024. 2

    2024 arXiv

  22. [30]

    SDXL: improving latent diffusion models for high-resolution image synthesis

    Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas M ¨uller, Joe Penna, and Robin Rombach. SDXL: improving latent diffusion models for high-resolution image synthesis. In The Twelfth Interna- tional Conference on Learning Representations, ICLR 2024,

    2024

  23. [31]

    Learning transferable visual models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In 38th Interna- tio...

    2021

  24. [32]

    High-resolution image syn- thesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj¨orn Ommer. High-resolution image syn- thesis with latent diffusion models. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 , pages 10674–10685, 2022. 3

    2022

  25. [33]

    Adversarial diffusion distillation

    Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach. Adversarial diffusion distillation. In Proceedings of European Conference on Computer Vision, pages 87–103,

  26. [34]

    Denois- ing diffusion implicit models

    Jiaming Song, Chenlin Meng, and Stefano Ermon. Denois- ing diffusion implicit models. In 9th International Confer- ence on Learning Representations, ICLR 2021, 2021. 3

    2021

  27. [35]

    Blindly assess image qual- ity in the wild guided by a self-adaptive hyper network

    Shaolin Su, Qingsen Yan, Yu Zhu, Cheng Zhang, Xin Ge, Jinqiu Sun, and Yanning Zhang. Blindly assess image qual- ity in the wild guided by a self-adaptive hyper network. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, pages 3664–3673, 2020. 6

    2020

  28. [36]

    Jianyi Wang, Kelvin C. K. Chan, and Chen Change Loy. Ex- ploring CLIP for assessing the look and feel of images. In Thirty-Seventh AAAI Conference on Artificial Intelligence , pages 2555–2563, 2023. 2, 4

    2023

  29. [37]

    Jianyi Wang, Zongsheng Yue, Shangchen Zhou, Kelvin C. K. Chan, and Chen Change Loy. Exploiting diffusion prior for real-world image super-resolution. Int. J. Comput. Vis., 132 (12):5929–5949, 2024. 2, 3, 5

    2024

  30. [38]

    ESRGAN: enhanced super-resolution generative adversarial networks

    Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy. ESRGAN: enhanced super-resolution generative adversarial networks. In Proceedings of European Conference on Computer Vision Workshops, ECCVW 2018, pages 63–79, 2018. 2

    2018

  31. [39]

    Real-esrgan: Training real-world blind super-resolution with pure synthetic data

    Xintao Wang, Liangbin Xie, Chao Dong, and Ying Shan. Real-esrgan: Training real-world blind super-resolution with pure synthetic data. In IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021, pages 1905– 1914, 2021. 2, 5, 6, 11

    2021

  32. [40]

    Kot, and Bihan Wen

    Yufei Wang, Wenhan Yang, Xinyuan Chen, Yaohui Wang, Lanqing Guo, Lap-Pui Chau, Ziwei Liu, Yu Qiao, Alex C. Kot, and Bihan Wen. Sinsr: Diffusion-based image super- resolution in a single step. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024 , pages ...

    2024

  33. [41]

    Bovik, Hamid R

    Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process., 13(4): 600–612, 2004. 6

    2004

  34. [42]

    Zhihao Wang, Jian Chen, and Steven C. H. Hoi. Deep learn- ing for image super-resolution: A survey. IEEE Trans. Pat- tern Anal. Mach. Intell., 43(10):3365–3387, 2021. 2

    2021

  35. [43]

    Component divide- and-conquer for real-world image super-resolution

    Pengxu Wei, Ziwei Xie, Hannan Lu, Zongyuan Zhan, Qixi- ang Ye, Wangmeng Zuo, and Liang Lin. Component divide- and-conquer for real-world image super-resolution. In Pro- ceedings of European Conference on Computer Vision, pages 101–117, 2020. 5, 11

    2020

  36. [44]

    Q-align: Teaching lmms for visual scoring via discrete text-defined levels

    Haoning Wu, Zicheng Zhang, Weixia Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Yixuan Gao, Annan Wang, Erli Zhang, Wenxiu Sun, Qiong Yan, Xiongkuo Min, Guang- tao Zhai, and Weisi Lin. Q-align: Teaching lmms for visual scoring via discrete text-defined levels. InForty-first Int...

    2024

  37. [45]

    One-step effective diffusion network for real-world image super-resolution

    Rongyuan Wu, Lingchen Sun, Zhiyuan Ma, and Lei Zhang. One-step effective diffusion network for real-world image super-resolution. Advances in Neural Information Process- ing Systems, 2024. 2, 3, 4, 5

    2024

  38. [46]

    Seesr: Towards semantics-aware 10 real-world image super-resolution

    Rongyuan Wu, Tao Yang, Lingchen Sun, Zhengqiang Zhang, Shuai Li, and Lei Zhang. Seesr: Towards semantics-aware 10 real-world image super-resolution. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024 , pages 25456–25467, 2024. 2, 3, 5

    2024

  39. [47]

    Addsr: Accelerating diffusion-based blind super-resolution with adversarial diffusion distillation

    Rui Xie, Ying Tai, Chen Zhao, Kai Zhang, Zhenyu Zhang, Jun Zhou, Xiaoqian Ye, Qian Wang, and Jian Yang. Addsr: Accelerating diffusion-based blind super-resolution with adversarial diffusion distillation. ArXiv preprint , abs/2404.01717, 2024. 2, 3, 4, 5

    2024 arXiv

  40. [48]

    Pixel-aware stable diffusion for realistic image super-resolution and personalized stylization

    Tao Yang, Rongyuan Wu, Peiran Ren, Xuansong Xie, and Lei Zhang. Pixel-aware stable diffusion for realistic image super-resolution and personalized stylization. In Proceed- ings of European Conference on Computer Vision, pages 74– 91, 2024. 2, 3, 5

    2024

  41. [49]

    Scaling up to excellence: Practicing model scaling for photo- realistic image restoration in the wild

    Fanghua Yu, Jinjin Gu, Zheyuan Li, Jinfan Hu, Xiangtao Kong, Xintao Wang, Jingwen He, Yu Qiao, and Chao Dong. Scaling up to excellence: Practicing model scaling for photo- realistic image restoration in the wild. In IEEE/CVF Confer- ence on Computer Vision and Pattern Recognit...

    2024

  42. [50]

    Resshift: Efficient diffusion model for image super- resolution by residual shifting

    Zongsheng Yue, Jianyi Wang, and Chen Change Loy. Resshift: Efficient diffusion model for image super- resolution by residual shifting. In Advances in Neural In- formation Processing Systems, 2023. 3, 5

    2023

  43. [51]

    Degradation-guided one-step image super-resolution with diffusion priors

    Aiping Zhang, Zongsheng Yue, Renjing Pei, Wenqi Ren, and Xiaochun Cao. Degradation-guided one-step image super-resolution with diffusion priors. ArXiv preprint , abs/2409.17058, 2024. 2, 3, 4, 5

    2024 arXiv

  44. [52]

    Designing a practical degradation model for deep blind im- age super-resolution

    Kai Zhang, Jingyun Liang, Luc Van Gool, and Radu Timofte. Designing a practical degradation model for deep blind im- age super-resolution. InIEEE/CVF International Conference on Computer Vision, ICCV 2021, pages 4771–4780, 2021. 2, 6, 11

    2021

  45. [53]

    Adding conditional control to text-to-image diffusion models

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In IEEE/CVF International Conference on Computer Vision, ICCV 2023, pages 3813–3824, 2023. 2, 3

    2023

  46. [54]

    Efros, Eli Shecht- man, and Oliver Wang

    Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shecht- man, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018 , pages 586–595, 2018. 2, 6 Appendix A. Comparison ...

    2018

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Reviewed August 11, 2026 · model on record in the stance chip above.