Pith. sign in

REVIEW 3 major objections 5 minor 42 references

RASR: Retrieval-Augmented Super Resolution for Practical Reference-based Image Restoration

T0 review · 3 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper introduces retrieval-augmented super-resolution, in which a semantic retriever automatically finds a high-resolution reference from a category-aligned database and a diffusion generator uses it to produce more realistic textures,

desk verdict Real new benchmark, solid baseline, but the abstract's 'consistent improvement over SISR baselines' overstates the evidence: the +0.38 dB is only vs OSEDiff, and RASRNet loses PSNR/SSIM to GAN-based SISR. read the letter →

arxiv 2508.09449 v2 pith:5JIZGODY submitted 2025-08-13 cs.CV

Jiaqi Yan , Shuning Xu , Xiangyu Chen , Dell Zhang , Jiantao Zhou , Jie Tang , Gangshan Wu , Jie Liu This is my paper · ORCID
classification cs.CV
keywords reference-basedsuper-resolutionretrieval-augmenteddiffusionmodelssemanticretrievalDINOv2imagerestorationRASR-Flickr30texturetransfer
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 asks whether reference-based super-resolution can work when the reference image is not hand-paired with the target but automatically retrieved from a database. It defines a new task, retrieval-augmented super-resolution (RASR), and argues that in real-world settings such as zoo or museum photos, a category-organized collection of high-resolution images is a realistic resource. Its system, RASRNet, retrieves the nearest-neighbor image in a self-supervised visual embedding space (DINOv2) and injects that reference into a frozen diffusion super-resolution model through a ControlNet-style side branch, using global semantic information rather than patch-level matching. On the new RASR-Flickr30 benchmark, the full method beats its single-image baseline by +0.38 dB PSNR and reduces the perceptual metric LPIPS by 0.0131, with the largest gains in texture realism. The central claim is that retrieval-augmented semantic conditioning, not pre-paired references, makes reference-based super-resolution practical.

What carries the argument

The load-bearing object is the retrieval-to-generation interface: a semantic retriever (RIRR) built on DINOv2 embeddings with cosine-similarity nearest-neighbor search over a per-category database, feeding a reference-conditioned diffusion generator (RefDSR). RefDSR freezes a pretrained one-step diffusion SISR model and injects the VAE-encoded reference latent through a trainable ControlNet branch with LoRA and zero convolutions, adding reference features only to the first three UNet decoder blocks (with a 0.5 fusion weight at inference) while text cross-attention acts on the final block. The design deliberately avoids patch matching, and the ablation shows that the retrieval encoder matters

What would settle it

Hold out several species from the reference database and run RASRNet on those queries with retrieval forced to return the nearest other species; if PSNR and LPIPS are no better than the OSEDiff baseline on those held-out species, the claim that semantic retrieval from a category-aligned database produces the gains is overturned.

Watch

Extended reading notes

Core claim

The paper's central claim is that global semantic retrieval is enough to make reference information useful in diffusion-based super-resolution, even without structural correspondence between the low-resolution input and the reference. RASRNet's retriever compares DINOv2 embeddings by cosine similarity to pick one reference from a per-category database; the generator then encodes that reference with a frozen VAE, processes it in a LoRA-tuned ControlNet branch, and adds its features to the first three decoder blocks of a frozen one-step diffusion SISR model, with text prompts from both the low-resolution input and the reference combined at the final block. Training uses MSE, LPIPS, Gram, and G

Load-bearing premise

The load-bearing premise is that a semantically relevant high-resolution photo exists in the reference database and that global semantic similarity, measured on the degraded input, is enough to make that photo useful; if the query's category is missing or the nearest neighbor is only superficially similar, the generator receives misleading reference features.

Editorial extensions

If this is right

  • Reference-based super-resolution no longer requires pre-paired target-reference images; a category-aligned image database and a semantic retriever are enough to get consistent gains.
  • Perceptual quality metrics improve more than fidelity metrics, so retrieval augmentation is most useful for texture realism rather than raw reconstruction accuracy.
  • Existing patch-matching RefSR models are not robust to realistic degradations when references are retrieved, so the field needs retrieval-friendly, semantic-conditioning generators.
  • Combined text prompts from the low-res input and the retrieved reference beat either alone, so retrieval also helps with semantic conditioning, not just image features.
  • The benchmark opens a way to compare RefSR methods in open-world retrieval settings rather than only fixed-pair datasets.

Reading between the lines

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

  • Extending beyond the paper's experiments: the same semantic-retrieval-plus-diffusion recipe could transfer to other category-structured domains such as museum artifacts or plant species, but only with a database whose categories cover the query; the paper tests only 30 animal species.
  • The paper's own limitation note—fine scene text and subtle semantics are poorly restored—suggests global semantic retrieval will help least where pixel-level structure dominates; a targeted test on text-heavy crops would likely show the retrieval gain shrinking.
  • Because the retriever uses a single nearest neighbor, the method leaves untested whether multiple retrieved references, or a smaller per-category representative set, would give the same or better gains; database diversity versus retrieval quality is therefore an open question.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper introduces RASR (Retrieval-Augmented Super Resolution), a paradigm in which a low-resolution input is used to retrieve a semantically relevant high-resolution reference from a prebuilt database, and a diffusion-based generator, RASRNet, conditions on that reference to produce a high-resolution output. The authors also present RASR-Flickr30, a 30-species animal dataset with per-category reference galleries, and report experiments on this dataset plus WR-SR. The central claim is that RASRNet consistently improves over SISR baselines and that automatic retrieval makes RefSR practical without manually paired references.

Significance. If the claims were fully supported, the work would be a useful step toward practical reference-based super-resolution: it removes the need for paired target-reference images during deployment, contributes a new benchmark with per-category retrieval galleries, and demonstrates that a frozen diffusion SISR backbone can be augmented with retrieved references through a lightweight ControlNet/LoRA branch. The ablations on retrieval encoders, loss components, and prompt fusion are informative and help justify several design choices. However, the headline quantitative claim is overstated: on fidelity metrics the method does not improve over GAN-based SISR baselines, and the reported +0.38 dB gain is only relative to OSEDiff. The practical/open-world framing also goes beyond what the 30-species animal dataset can demonstrate. The core idea remains promising, but the evidence as presented requires substantial reframing and additional experiments.

major comments (3)
  1. [Abstract; Table 1; §5.1] The abstract and conclusion state that RASRNet 'consistently improves over SISR baselines', but Table 1 contradicts this for fidelity metrics. RASRNet has PSNR 23.67 dB and SSIM 0.5687, which are below BSRGAN (24.72/0.5952), Real-ESRGAN (24.78/0.6078), and HAT (25.07/0.6160). The +0.38 dB improvement is specifically relative to OSEDiff, not to SISR baselines in general. The evidence supports improved perceptual quality over diffusion-based SISR methods, not consistent fidelity improvement. Please revise the claims accordingly or provide an apples-to-apples comparison that supports the original wording.
  2. [§4 Training; §3.2; Eq. (6)] There are two train/inference mismatches that are not ablated. First, during training the five reference images are 'pre-selected based on cosine similarity to the GT image', while at inference RIRR retrieves using the degraded LR input. Since retrieval quality under real degradations is a central claim, the robustness of LR-based retrieval should be measured directly (e.g., compare GT-retrieved vs LR-retrieved references in the same generation pipeline). Second, Eq. (2) uses fusion weight 1.0 during training, while Eq. (6) uses 0.5 at inference. The choice of 0.5 is described as 'balanced' but no ablation is provided. These gaps leave the main deployed configuration untested.
  3. [§4 RASR-Flickr30; Introduction] The practical and 'open-world' framing is not supported by the evaluation. RASR-Flickr30 contains only 30 animal species, the test set is drawn from the same categories as the reference database, and there is no retrieval experiment for queries outside those categories or for the museum/artwork scenarios used as motivation. The claim that RASR enables 'arbitrary LR input' or 'open-world retrieval' therefore extrapolates beyond the closed-category setting tested. Either add a cross-category or out-of-domain retrieval evaluation, or substantially temper the generality claims in the title, abstract, and introduction.
minor comments (5)
  1. [§5, Table 4] The loss ablation shows that adding Gram and GAN losses reduces PSNR from 24.35 (MSE+LPIPS only) to 23.67 (full model). This is a fidelity-perception tradeoff that should be acknowledged explicitly, especially given the paper's emphasis on PSNR improvements elsewhere.
  2. [§4 Dataset] The dataset description lacks quantitative statistics: total number of images, per-species image counts, resolution distribution, and licensing information. Since the dataset is a contribution, these details should be provided.
  3. [References] References [Zhang et al. 2019a] and [Zhang et al. 2019b] are listed with identical titles ('Image super-resolution by neural texture transfer') and are likely the same work; please consolidate or distinguish them.
  4. [§5.1 'Retrieval Encoder Comparison'] The phrase 'open-set reference retrieval' is used, but the retrieval setting is closed-category. Consider using 'open-retrieval within a category-aligned gallery' to avoid overclaiming.
  5. [§3.2 'Inference Process'] The 'fine-tuned text extractor' mentioned in the inference discussion is not specified. Please state the architecture, training data, and whether a public model is used, so the prompt-fusion ablation is reproducible.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation; the +0.38 dB claim is an overstatement but not a circular reduction.

full rationale

The paper does not derive its reported results from its own premises in a circular way. Equation (1) is a task definition, not a derivation. The retriever uses a fixed external DINOv2 encoder and the generator uses a frozen OSEDiff backbone with a trainable ControlNet branch; these are independent, externally pretrained components. The training detail that 'five reference images are pre-selected based on cosine similarity to the GT image' is an oracle-style training choice, but at inference the paper states 'we encode the LR input using the same vision encoder' and retrieves with the LR, so the test numbers are not computed by feeding the GT into the retriever. The RefDSR generator is also evaluated on the external WR-SR benchmark (Table 2), where it improves over diffusion SISR baselines, providing independent validation beyond the self-built RASR-Flickr30. The only self-citation is HAT (Chen et al. 2023), which shares author Xiangyu Chen; it is used only as a comparison baseline and is not load-bearing. The abstract's claim of 'consistently improves over SISR baselines' is overbroad because Table 1 shows RASRNet's PSNR/SSIM are below GAN-based SISR methods, and the +0.38 dB is relative to OSEDiff only; however, this is a reporting/overclaim issue, not a circularity. The self-authored benchmark is a validity limitation, but it does not make the derivation equivalent to its inputs. Score 2 reflects the minor non-load-bearing self-citation and the self-built benchmark, not an actual circular step.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The paper's central claim is empirical, so the main debts are assumptions about the deployment environment: a category-aligned database exists, DINOv2 global embeddings select useful references under degradation, LR+REF text prompts are reliable, and a frozen OSEDiff backbone can be conditioned effectively. Hyperparameters such as the 0.5 fusion weight and loss scalars are hand-set and not ablated.

free parameters (3)
  • Inference fusion weight = 0.5
    Eq. 6 introduces a scalar 0.5 to balance original and reference-modulated features at inference; no ablation or sensitivity analysis is provided for this value.
  • Loss weights lambda_LPIPS, lambda_Gram, lambda_GAN = 2, 1e-3, 0.1
    These weights in Eq. 5 are hand-set to balance fidelity and perceptual quality; no sensitivity analysis is reported.
  • LoRA rank = 4
    LoRA rank is set to 4 without comparison, affecting the capacity of the ControlNet conditioning branch.
assumptions (3)
  • domain assumption A pre-curated, category-aligned reference database is available in target deployment scenarios
    The Introduction motivates zoos, museums, and exhibitions, and the entire RASR-Flickr30 benchmark assumes this; the practical significance claim depends on it.
  • domain assumption Cosine similarity in DINOv2 embedding space identifies references that improve diffusion-based SR under realistic degradations
    Section 3.2 argues semantic retrieval is optimal, but this is only tested on 30 animal species, not on scenes, text, or arbitrary open-world content.
  • domain assumption Text prompts extracted from LR and REF provide valid, robust conditioning
    The inference process combines LR and reference prompts (Table 3), relying on a fine-tuned text extractor that is not described in detail and is acknowledged as unreliable in some cases.

how reviews work

0 comments
Cite this review

Pith. "Pith review of RASR: Retrieval-Augmented Super Resolution for Practical Reference-based Image Restoration." pith.science (2026). https://pith.science/paper/5JIZGODY

@misc{pith2026250809449,
  author       = {Pith},
  title        = {Pith review of: RASR: Retrieval-Augmented Super Resolution for Practical Reference-based Image Restoration},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5JIZGODY}},
  note         = {Machine review of arXiv:2508.09449}
}
read the original abstract

Reference-based Super Resolution (RefSR) improves upon Single Image Super Resolution (SISR) by leveraging high-quality reference images to enhance texture fidelity and visual realism. However, a critical limitation of existing RefSR approaches is their reliance on manually curated target-reference image pairs, which severely constrains their practicality in real-world scenarios. To overcome this, we introduce Retrieval-Augmented Super Resolution (RASR), a new and practical RefSR paradigm that automatically retrieves semantically relevant high-resolution images from a reference database given only a low-quality input. This enables scalable and flexible RefSR in realistic use cases, such as enhancing mobile photos taken in environments like zoos or museums, where category-specific reference data (e.g., animals, artworks) can be readily collected or pre-curated. To facilitate research in this direction, we construct RASR-Flickr30, the first benchmark dataset designed for RASR. Unlike prior datasets with fixed target-reference pairs, RASR-Flickr30 provides per-category reference databases to support open-world retrieval. We further propose RASRNet, a strong baseline that combines a semantic reference retriever with a diffusion-based RefSR generator. It retrieves relevant references based on semantic similarity and employs a diffusion-based generator enhanced with semantic conditioning. Experiments on RASR-Flickr30 demonstrate that RASRNet consistently improves over SISR baselines, achieving +0.38 dB PSNR and -0.0131 LPIPS, while generating more realistic textures. These findings highlight retrieval augmentation as a promising direction to bridge the gap between academic RefSR research and real-world applicability.

Figures

Figures reproduced from arXiv: 2508.09449 by the authors.

Figure 1
Figure 1. Visual comparison between RASRNet and the [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. (a) Example images from the proposed RASR-Flickr30 dataset. (b) Illustration of the retrieval process, where a target [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Overall architecture of RASRNet. It consists of two modules: Real-world Image Reference Retriever (RIRR) and [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Qualitative comparisons of different diffusion-based SR methods. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

42 extracted references · 11 canonical work pages

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

  3. [3]

    Barnes, C.; Shechtman, E.; Finkelstein, A.; and Goldman, D. B. 2009. PatchMatch: A randomized correspondence algorithm for structural image editing. ACM Trans. Graph., 28(3): 24

    work page 2009

  4. [4]

    Cao, J.; Liang, J.; Zhang, K.; Li, Y.; Zhang, Y.; Wang, W.; and Gool, L. V. 2022. Reference-based image super-resolution with deformable attention transformer. In European conference on computer vision, 325--342. Springer

    2022

  5. [5]

    Caron, M.; Touvron, H.; Misra, I.; J \'e gou, H.; Mairal, J.; Bojanowski, P.; and Joulin, A. 2021. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, 9650--9660

    2021

  6. [6]

    Chen, X.; Wang, X.; Zhou, J.; Qiao, Y.; and Dong, C. 2023. Activating more pixels in image super-resolution transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 22367--22377

    2023

  7. [7]

    Dai, J.; Qi, H.; Xiong, Y.; Li, Y.; Zhang, G.; Hu, H.; and Wei, Y. 2017. Deformable convolutional networks. In Proceedings of the IEEE international conference on computer vision, 764--773

    2017

  8. [8]

    Ding, K.; Ma, K.; Wang, S.; and Simoncelli, E. P. 2020. Image quality assessment: Unifying structure and texture similarity. IEEE transactions on pattern analysis and machine intelligence, 44(5): 2567--2581

    2020

Show all 42 references
  1. [9]

    Heusel, M.; Ramsauer, H.; Unterthiner, T.; Nessler, B.; and Hochreiter, S. 2017. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30

    2017

  2. [10]

    J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; Chen, W.; et al

    Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; Chen, W.; et al. 2022. Lora: Low-rank adaptation of large language models. ICLR, 1(2): 3

    2022

  3. [11]

    Huang, J.-B.; Singh, A.; and Ahuja, N. 2015. Single image super-resolution from transformed self-exemplars. In Proceedings of the IEEE conference on computer vision and pattern recognition, 5197--5206

    2015

  4. [12]

    C.; Wang, X.; Loy, C

    Jiang, Y.; Chan, K. C.; Wang, X.; Loy, C. C.; and Liu, Z. 2021. Robust reference-based super-resolution via c2-matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2103--2112

    2021

  5. [13]

    Ke, J.; Wang, Q.; Wang, Y.; Milanfar, P.; and Yang, F. 2021. Musiq: Multi-scale image quality transformer. In Proceedings of the IEEE/CVF international conference on computer vision, 5148--5157

    2021

  6. [14]

    Kumari, N.; Zhang, R.; Shechtman, E.; and Zhu, J.-Y. 2022. Ensembling off-the-shelf models for gan training. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 10651--10662

    2022

  7. [15]

    Li, Y.; Zhang, K.; Liang, J.; Cao, J.; Liu, C.; Gong, R.; Zhang, Y.; Tang, H.; Liu, Y.; Demandolx, D.; et al. 2023. Lsdir: A large scale dataset for image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1775--1787

    2023

  8. [16]

    Liang, J.; Cao, J.; Sun, G.; Zhang, K.; Van Gool, L.; and Timofte, R. 2021. Swinir: Image restoration using swin transformer. In Proceedings of the IEEE/CVF international conference on computer vision, 1833--1844

    2021

  9. [17]

    Lin, X.; He, J.; Chen, Z.; Lyu, Z.; Dai, B.; Yu, F.; Qiao, Y.; Ouyang, W.; and Dong, C. 2024. Diffbir: Toward blind image restoration with generative diffusion prior. In European Conference on Computer Vision, 430--448. Springer

    2024

  10. [18]

    Liu, Z.; Lin, Y.; Cao, Y.; Hu, H.; Wei, Y.; Zhang, Z.; Lin, S.; and Guo, B. 2021. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, 10012--10022

    2021

  11. [19]

    Loshchilov, I.; and Hutter, F. 2017. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101

    2017 arXiv

  12. [20]

    Matsui, Y.; Ito, K.; Aramaki, Y.; Fujimoto, A.; Ogawa, T.; Yamasaki, T.; and Aizawa, K. 2017. Sketch-based manga retrieval using manga109 dataset. Multimedia tools and applications, 76: 21811--21838

    2017

  13. [21]

    Oquab, M.; Darcet, T.; Moutakanni, T.; Vo, H.; Szafraniec, M.; Khalidov, V.; Fernandez, P.; Haziza, D.; Massa, F.; El-Nouby, A.; et al. 2023. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193

    2023 arXiv

  14. [22]

    W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al

    Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning, 8748--8763. PmLR

    2021

  15. [23]

    Shim, G.; Park, J.; and Kweon, I. S. 2020. Robust reference-based super-resolution with similarity-aware deformable convolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 8425--8434

    2020

  16. [24]

    Simonyan, K.; and Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

    2014 arXiv

  17. [25]

    Sun, L.; and Hays, J. 2012. Super-resolution from internet-scale scene matching. In 2012 IEEE International conference on computational photography (ICCP), 1--12. IEEE

    2012

  18. [26]

    C.; and Loy, C

    Wang, J.; Chan, K. C.; and Loy, C. C. 2023. Exploring clip for assessing the look and feel of images. In Proceedings of the AAAI conference on artificial intelligence, volume 37, 2555--2563

    2023

  19. [27]

    C.; and Loy, C

    Wang, J.; Yue, Z.; Zhou, S.; Chan, K. C.; and Loy, C. C. 2024 a . Exploiting diffusion prior for real-world image super-resolution. International Journal of Computer Vision, 132(12): 5929--5949

    2024

  20. [28]

    Wang, X.; Xie, L.; Dong, C.; and Shan, Y. 2021. Real-esrgan: Training real-world blind super-resolution with pure synthetic data. In Proceedings of the IEEE/CVF international conference on computer vision, 1905--1914

    2021

  21. [29]

    C.; and Wen, B

    Wang, Y.; Yang, W.; Chen, X.; Wang, Y.; Guo, L.; Chau, L.-P.; Liu, Z.; Qiao, Y.; Kot, A. C.; and Wen, B. 2024 b . Sinsr: diffusion-based image super-resolution in a single step. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 25796--25805

    2024

  22. [30]

    Wu, R.; Sun, L.; Ma, Z.; and Zhang, L. 2024 a . One-step effective diffusion network for real-world image super-resolution. Advances in Neural Information Processing Systems, 37: 92529--92553

    2024

  23. [31]

    Wu, R.; Yang, T.; Sun, L.; Zhang, Z.; Li, S.; and Zhang, L. 2024 b . Seesr: Towards semantics-aware real-world image super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 25456--25467

    2024

  24. [32]

    Yang, F.; Yang, H.; Fu, J.; Lu, H.; and Guo, B. 2020. Learning texture transformer network for image super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 5791--5800

    2020

  25. [33]

    Yang, T.; Wu, R.; Ren, P.; Xie, X.; and Zhang, L. 2024. Pixel-aware stable diffusion for realistic image super-resolution and personalized stylization. In European Conference on Computer Vision, 74--91. Springer

    2024

  26. [34]

    Yu, F.; Gu, J.; Li, Z.; Hu, J.; Kong, X.; Wang, X.; He, J.; Qiao, Y.; and Dong, C. 2024. Scaling up to excellence: Practicing model scaling for photo-realistic image restoration in the wild. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, ...

    2024

  27. [35]

    Zhang, K.; Liang, J.; Van Gool, L.; and Timofte, R. 2021. Designing a practical degradation model for deep blind image super-resolution. In Proceedings of the IEEE/CVF international conference on computer vision, 4791--4800

    2021

  28. [36]

    Zhang, L.; Li, X.; He, D.; Li, F.; Ding, E.; and Zhang, Z. 2023. LMR: a large-scale multi-reference dataset for reference-based super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 13118--13127

    2023

  29. [37]

    Zhang, L.; Rao, A.; and Agrawala, M. 2023. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF international conference on computer vision, 3836--3847

    2023

  30. [38]

    Zhang, L.; Zhang, L.; and Bovik, A. C. 2015. A feature-enriched completely blind image quality evaluator. IEEE Transactions on Image Processing, 24(8): 2579--2591

    2015

  31. [39]

    A.; Shechtman, E.; and Wang, O

    Zhang, R.; Isola, P.; Efros, A. A.; Shechtman, E.; and Wang, O. 2018. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, 586--595

    2018

  32. [40]

    Zhang, Z.; Wang, Z.; Lin, Z.; and Qi, H. 2019 a . Image super-resolution by neural texture transfer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 7982--7991

    2019

  33. [41]

    Zhang, Z.; Wang, Z.; Lin, Z.; and Qi, H. 2019 b . Image super-resolution by neural texture transfer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 7982--7991

    2019

  34. [42]

    Zheng, H.; Ji, M.; Wang, H.; Liu, Y.; and Fang, L. 2018. Crossnet: An end-to-end reference-based super resolution network using cross-scale warping. In Proceedings of the European conference on computer vision (ECCV), 88--104

    2018

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.