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Universal Image Restoration Pre-training via Degradation Classification

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper argues that classifying the type of degradation in an image is a nearly free and highly effective pre-training objective for universal image restoration, yielding consistent gains of over 2 dB across architectures and as much…

desk verdict A practical pre-training recipe for restoration with consistent gains, but the headline numbers need an equal-compute control before I'd trust the magnitude. read the letter →

arxiv 2501.15510 v1 pith:V7A74XFA submitted 2025-01-26 cs.CV

JiaKui Hu , Lujia Jin , Zhengjian Yao , Yanye Lu This is my paper · ORCID
classification cs.CV
keywords imagerestorationdegradationclassificationpre-trainingall-in-onemixedtransferlearningweaksupervision
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

This paper argues that classifying the type of degradation in an input image is a powerful and nearly free pre-training signal for universal image restoration. The proposed DCPT method first trains a restoration network's encoder so that a lightweight decoder can name the degradation (haze, rain, noise, blur, low light, and more) from multi-level features, while a pixel-reconstruction loss preserves the ability to generate clean images. Across five-task and ten-task all-in-one (single model, many degradations) benchmarks, DCPT raises CNN and Transformer backbones by at least 2.08 dB on average, reaching 2.55 dB on the ten-task setting, and produces an average gain of 6.53 dB across the 11 degradation categories of the CDD mixed-degradation benchmark. The wider point is that restoration models already contain degradation-discrimination information, and making that information explicit before fine-tuning improves restoration and transfer across unseen and mixed damage.

What carries the argument

The central mechanism is the degradation classifier that DCPT retains after pre-training instead of discarding it as masked-image-modelling methods do. Named DCPT, the framework has two alternating stages per iteration: (a) a classification stage in which a lightweight decoder (ResNet18 with LayerNorm) predicts the degradation from scaled multi-level encoder features under Focal Loss, and (b) a generation stage in which a convolution maps the deepest feature to a clean image under an L1 pixel loss. The retained classifier is then used in DC-guided training, where a frozen decoder adds a cross-entropy term during fine-tuning so the model continues to distinguish clean from degraded inputs; this term is what carries cross-degradation transfer.

What would settle it

Run an equal-compute control in which the same backbone trains from scratch on the same paired data for 850,000 iterations with no classification loss; if its PSNR matches or surpasses DCPT's fine-tuned numbers, the degradation-classification signal is not the active ingredient.

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Extended reading notes

Core claim

The authors establish that degradation classification is a latent capability of image restoration networks and can be turned into a pre-training objective. Randomly initialized SwinIR, NAFNet, Restormer, and PromptIR classify five degradations at 52 to 71 percent accuracy, and the same models trained on a three-task all-in-one task exceed 94 percent while also recognizing unseen degradation types. DCPT exploits this by pre-training an encoder with a lightweight ResNet18-style classifier on multi-level features using Focal Loss, alternating with a simple pixel-generation stage; the encoder then initializes a restoration model, and the classifier is kept for DC-guided training. The paper's central claim is that this degradation-classification prior, rather than external prompt embeddings or generative priors, is what drives the observed restoration gains.

Load-bearing premise

The gains come from learning to classify degradation types, not merely from the extra 100,000 pre-training iterations on the same datasets that fine-tuning also uses.

Editorial extensions

If this is right

  • CNN and Transformer backbones both improve: average gains of 2.08 dB or more on five-task all-in-one restoration (Table 2).
  • On ten-task all-in-one restoration, DCPT-NAFNet improves the NAFNet baseline by 2.55 dB average PSNR, with gains on all ten degradations (Table 3, Tables 16-17).
  • On the CDD mixed-degradation benchmark, DCPT-NAFNet has the highest PSNR among the compared universal restoration models, with an average gain of 6.53 dB over the NAFNet baseline across the 11 degradation categories (Table 18).
  • DC-guided training shrinks the cross-task transfer penalty: a denoising-trained Restormer loses only 2.56 dB when transferred to deblurring with DC guidance, versus 7.48 dB without it (Table 8).
  • Pre-training needs only the degradation type already implicit in paired restoration datasets, and the paper reports 20 epochs of pre-training plus 50 epochs of fine-tuning, compared with 1200 epochs for the IDR baseline.

Reading between the lines

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

  • The paper leaves implicit that any paired restoration dataset already supplies the classification label, so DCPT could be applied to synthetic super-resolution or real-world mixed degradation by treating the corruption recipe or a coarse degradation tag as the class.
  • The correlation the paper reports between pre-training classification accuracy and final PSNR suggests classification accuracy on a held-out degradation set could be used as an early-stopping signal or as a cheap proxy for downstream restoration quality.
  • A direct testable extension is to keep the frozen classifier at inference time and use its predicted degradation distribution to route the restoration model to task-specific heads or prompts, which the DC-guided training results suggest would work.
  • If DCPT's gains come from classification prior rather than extra compute, then combining degradation classification with masked-image modeling or contrastive objectives in the same pre-training could compound the benefits; the paper does not test this.
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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

4 major / 5 minor

Summary. The paper proposes Degradation Classification Pre-Training (DCPT), a two-stage pre-training method for universal image restoration. In the classification stage, an encoder (a restoration backbone without its restoration head) is trained so that a lightweight decoder can predict the degradation type from multi-level encoder features; in the generation stage, the encoder is trained with an L1 pixel loss to reconstruct clean images. After 100k pre-training iterations, the encoder is fine-tuned for 750k iterations on downstream restoration tasks. The paper reports consistent PSNR/SSIM gains over base backbones on 5D all-in-one restoration (Table 2), 10D all-in-one restoration (Table 3, +2.55 dB), mixed-degradation CDD restoration (Table 7, up to +5.58 dB), single-task deblurring, and cross-task transfer with a retained, frozen classifier (Table 8). It also includes ablations on decoder architecture, multi-level feature extraction, and loss stages (Tables 9-12), together with a motivating kNN experiment suggesting that restoration models can classify degradation types.

Significance. If the empirical gains replicate under a properly matched training budget, this is a valuable and unusually simple result: a cheap, dataset-intrinsic label—degradation type—can serve as an effective pre-training signal for universal restoration, and keeping the classifier after pre-training enables a form of cross-task transfer. The paper's strengths include its breadth across CNN and Transformer backbones, the explicit ablations of the classification and generation stages (Tables 10-12), the monotonic relationship reported in Table 13, and the public release of code and models. There is no circularity in the evaluation: the pretext objective is degradation classification while the downstream metric is restoration quality on external test sets. The main open risk is that DCPT's protocol gives the pre-trained models 100k additional iterations of exposure to the same data, and the paper does not yet provide an equal-compute control.

major comments (4)
  1. [Appendix B.1, Appendix B.2; Tables 2, 3, 7, 15] DCPT is not compared against an equal-compute baseline. Appendix B.1 states that the pre-training dataset for the all-in-one setting is identical to the fine-tuning dataset, with only slight modifications in the sampler; Appendix B.2 states that DCPT runs for 100k iterations and the subsequent fine-tuning runs for 750k iterations. The baseline rows in Tables 2, 3, 7, and 15 are therefore trained for 750k iterations from scratch while DCPT models receive 100k additional iterations of exposure to the same training images plus an auxiliary classification and pixel-reconstruction loss. The DC-Train ablation in Table 12 also does not equalize total iterations. Please add from-scratch baselines trained for 850k iterations under the same data, patch sampling, batch size, and learning-rate schedule, and also report a matched-budget DC-Train variant. If the gains survive this control, the attribution to degradation classification is supported; without it, the empirical headline of the paper is not secured.
  2. [Appendix B.2; Tables 2, 3, 7] The provenance of the baseline numbers is unclear. The paper says that the authors adopt the same training policy for different backbones in the all-in-one setting, but it does not state whether the non-DCPT rows (SwinIR, NAFNet, Restormer, PromptIR, and the competing methods in Tables 2, 3, and 7) were re-trained by the authors under this exact policy or are quoted from their original papers. If they are quoted, differences in training duration, patch size, optimizer, or data sampler can account for part of the reported gap. Please specify which rows were produced under the shared protocol and, for any quoted numbers, re-train the corresponding backbone under the same protocol so that the comparison is controlled.
  3. [Section 3.2 vs Appendix D] The method description and the pseudocode disagree on the optimization procedure. Section 3.2 says that the classification and generation stages are alternated within one pre-training iteration to avoid the encoder receiving two distinct gradient flows simultaneously. In the code in Appendix D, however, l_pix and l_cls are accumulated into the same l_total and one backward pass is executed through both encoder forward computations, so the encoder receives gradients from both losses in the same optimizer step. Please clarify which protocol was used in the experiments and align the text with the released code; this affects reproducibility and the interpretation of the stage ablation in Table 11.
  4. [Section 4.5 and Table 8] The transfer-learning experiment lacks a complete protocol. Appendix B.2 specifies the source-task training (100k iterations, batch size 8) but does not give the target-task training budget, learning-rate schedule, patch size, or the initialization protocol for the Supervised row. The large DC-guided gains in Table 8, for example the improvement from 25.44 dB to 30.36 dB for denoising-to-deblurring transfer, can only be interpreted if both rows use the same number of target-task iterations and the same fine-tuning schedule. Please provide these details and include matched-budget baselines.
minor comments (5)
  1. [Section 3.1, Section 3.2, Figure 6] There are several typos: 'unkown' in Section 3.1 should be 'unknown'; 'pipline' in Section 3.2 should be 'pipeline'; and 'Insturct-NAFNet' in the Figure 6 legend should be 'Instruct-NAFNet'.
  2. [Table 4] The asterisk on 'PromptIR*' is never defined; please add a footnote explaining what it means.
  3. [Table 13] Table 13 reports a single trajectory without error bars or a statement about seeds; given that the main tables also report single runs, please state the number of seeds or provide variance estimates for the headline experiments.
  4. [Section 4.5] The sentence 'The results of the non-DC-guided experiments presents' should use the singular verb 'present'.
  5. [Appendix A.3] The monotonic relationship in Table 13 is correlational: increasing pre-training iterations simultaneously changes classification accuracy, feature quality, and the amount of pixel-reconstruction exposure, so the causal wording 'performance improved as the initial degradation classification accuracy increased' should be tempered or supported by an intervention that decouples classification accuracy from iteration count.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: DCPT's gains are measured on external test sets and the pretext label (degradation type) is distinct from the downstream target (clean image).

full rationale

The paper's derivation chain is: (1) observe that restoration features carry degradation-classification information via kNN; (2) add a degradation-classification loss plus a generation loss during pre-training; (3) fine-tune on restoration tasks and evaluate on SOTS, GoPro, BSD68, and CDD test sets. None of these steps defines the target quantity in terms of the input. The degradation label Dgt is a different supervision signal from the pixel target xclean, and the encoder is not fitted to the test metrics. Ablations in Tables 11 and 12 compare against from-scratch training and DC-Train, so the reported gains are not constructed by the loss definition. Self-citations such as Hu et al. 2024 and Yao et al. 2025 appear only as generic references for scaling and architecture choices and are not load-bearing. The main weakness is the absence of an equal-compute control, since DCPT sees 100k additional iterations on the same data (Appendix B.1/B.2), but that is an experimental-control issue about isolating the cause of the gain, not a circular reduction of a prediction to its input. Therefore no circularity is identified.

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

The central claim rests on a small set of training hyperparameters (loss weight, pre-training length, learning rates, sampling ratios) and a few domain assumptions about the availability and transferability of degradation labels. The paper introduces no new physical entities.

free parameters (4)
  • Loss weight alpha = 1
    Weight balancing pixel loss and focal classification loss in Eq. 4; default chosen by hand, no tuning reported.
  • Pre-training iterations = 100k
    DCPT runs 100k iterations (Appendix B.2); gains grow with iterations in Table 13, so this number is load-bearing for the reported improvements.
  • Learning rates (encoder/decoder) = 3e-4 / 1e-4
    Separate learning rates for encoder and decoder chosen by hand (Appendix B.2).
  • Dataset repeat ratios = e.g., [1H, 300R, 15N, 5B, 60L] for 5D
    Hand-chosen sampling ratios balance datasets of very different sizes; this affects what the classifier sees and the reported gains.
assumptions (4)
  • domain assumption Every image restoration dataset provides a degradation-type label for each image.
    DCPT relies on D_gt, the degradation type, as supervision (Eq. 3). True for synthetic benchmarks, not for in-the-wild data without labels.
  • domain assumption Degradation classification on encoder features is a useful surrogate for restoration ability; features that separate degradation types are the right features for restoration.
    The core motivating hypothesis in Sec. 3.1; validated only by the authors' experiments, not by an external principle.
  • ad hoc to paper A frozen classifier trained by DCPT remains valid when the encoder is fine-tuned or trained on a different degradation task, so it can guide transfer learning.
    DC-guided training (Sec. 3.3) freezes the decoder and adds classification loss during target-task training; its success assumes the classifier's feature alignment persists after encoder weight updates.
  • ad hoc to paper Identity reconstruction of clean images in the generation stage preserves useful representation.
    Figure 2(b) and Eq. 4 use clean images as input and target; the SG-only ablation collapses to identity, suggesting this stage does not teach restoration on its own.

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Cite this review

Pith. "Pith review of Universal Image Restoration Pre-training via Degradation Classification." pith.science (2026). https://pith.science/paper/V7A74XFA

@misc{pith2026250115510,
  author       = {Pith},
  title        = {Pith review of: Universal Image Restoration Pre-training via Degradation Classification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/V7A74XFA}},
  note         = {Machine review of arXiv:2501.15510}
}
read the original abstract

This paper proposes the Degradation Classification Pre-Training (DCPT), which enables models to learn how to classify the degradation type of input images for universal image restoration pre-training. Unlike the existing self-supervised pre-training methods, DCPT utilizes the degradation type of the input image as an extremely weak supervision, which can be effortlessly obtained, even intrinsic in all image restoration datasets. DCPT comprises two primary stages. Initially, image features are extracted from the encoder. Subsequently, a lightweight decoder, such as ResNet18, is leveraged to classify the degradation type of the input image solely based on the features extracted in the first stage, without utilizing the input image. The encoder is pre-trained with a straightforward yet potent DCPT, which is used to address universal image restoration and achieve outstanding performance. Following DCPT, both convolutional neural networks (CNNs) and transformers demonstrate performance improvements, with gains of up to 2.55 dB in the 10D all-in-one restoration task and 6.53 dB in the mixed degradation scenarios. Moreover, previous self-supervised pretraining methods, such as masked image modeling, discard the decoder after pre-training, while our DCPT utilizes the pre-trained parameters more effectively. This superiority arises from the degradation classifier acquired during DCPT, which facilitates transfer learning between models of identical architecture trained on diverse degradation types. Source code and models are available at https://github.com/MILab-PKU/dcpt.

Figures

Figures reproduced from arXiv: 2501.15510 by the authors.

Figure 1
Figure 1. The T-SNE results of randomly initialized PromptIR’s feature (left) and all-in-one trained [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. DCPT follows an encoder-decoder design. The encoder refers to a restoration network, and [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. DC-guided training is used for cross-degradation transfer learning. The target task in this figure is denoising. 5 [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Visual comparison on 5D all-in-one image restoration datasets. Zoom in for best view. and large diffusion model (Zheng et al., 2024), the latent degradation classification prior of the model trained with DCPT is more effective in addressing the all-in-one restoration t…
Figure 5
Figure 5. Figure 5: The radar chart of 10D all-in-one image restoration results. Method 10D-Average PSNR↑/SSIM↑ AirNet 26.41 / 0.842 TransWeather 22.83 / 0.779 WeatherDiff 24.60 / 0.793 PromptIR 27.93 / 0.851 DiffUIR-L 28.75 / 0.869 NAFNet 27.17 / 0.837 +DACLIP 27.42 / 0.798 +Instruct 28.…
Figure 6
Figure 6. Figure 6: Visual comparison on low-light + haze + rain samples. The DCPT enables the NAFNet to restore HQ images from mixed degradation while adjusting lighting to realistic conditions. In con￾trast, neither CLIP nor human-instruct can achieve both tasks concurrently. Zoom in fo…
Figure 7
Figure 7. Figure 7: Visual comparison on 3D all-in-one image restoration datasets. Top row: Gaussian color denoising on BSD68 (Martin et al., 2001a). Middle row: Image deraining on Test100L (Yang et al., 2019). Bottom row: Image dehazing on SOTS (Li et al., 2018). DCPT-PromptIR can remove…

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  2. IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models

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    A Gaussian-path transition equation lets a pretrained Stable Diffusion model serve as the denoiser inside image restoration bridges, cutting per-task training to a lightweight ControlNet.

  3. CURE: Controllable Unified Image Restoration for Complex Degradations

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    CURE adds four losses and an identity embedding so text-guided restorers can selectively and continuously control removal of composite image degradations without architecture changes.

  4. Diffusion Once and Done: Degradation-Aware LoRA for Efficient All-in-One Image Restoration

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    Proposes DOD, a one-step Stable Diffusion model for all-in-one image restoration, but the submitted manuscript text is an unrelated software engineering review, leaving the claim unverifiable.

Reference graph

Works this paper leans on

91 extracted references · 53 canonical work pages · cited by 4 Pith papers

  1. [1]

    A high-quality denoising dataset for smartphone cameras

    Abdelrahman Abdelhamed, Stephen Lin, and Michael S Brown. A high-quality denoising dataset for smartphone cameras. In CVPR, 2018

    2018

  2. [2]

    Defocus deblurring using dual-pixel data

    Abdullah Abuolaim and Michael S Brown. Defocus deblurring using dual-pixel data. In Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part X 16, pp.\ 111--126. Springer, 2020

    2020

  3. [3]

    Ntire 2017 challenge on single image super-resolution: Dataset and study

    Eirikur Agustsson and Radu Timofte. Ntire 2017 challenge on single image super-resolution: Dataset and study. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, July 2017

    2017

  4. [4]

    Multimodal prompt perceiver: Empower adaptiveness generalizability and fidelity for all-in-one image restoration

    Yuang Ai, Huaibo Huang, Xiaoqiang Zhou, Jiexiang Wang, and Ran He. Multimodal prompt perceiver: Empower adaptiveness generalizability and fidelity for all-in-one image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 25432--25444, 2024

    2024

  5. [5]

    Layer normalization

    Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. Layer normalization. arXiv preprint arXiv:1607.06450, 2016

    arXiv 2016

  6. [6]

    Language models are few-shot learners

    Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33: 0 1877--1901, 2020

    1901

  7. [7]

    A non-local algorithm for image denoising

    Antoni Buades, Bartomeu Coll, and J-M Morel. A non-local algorithm for image denoising. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05), volume 2, pp.\ 60--65. Ieee, 2005

    2005

  8. [8]

    Reversible column networks

    Yuxuan Cai, Yizhuang Zhou, Qi Han, Jianjian Sun, Xiangwen Kong, Jun Li, and Xiangyu Zhang. Reversible column networks. In International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=Oc2vlWU0jFY

    2023

Show all 91 references
  1. [9]

    Emerging properties in self-supervised vision transformers

    Mathilde Caron, Hugo Touvron, Ishan Misra, Herv\'e J\'egou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the International Conference on Computer Vision (ICCV), 2021

    2021

  2. [10]

    Pre-trained image processing transformer, 2021 a

    Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao. Pre-trained image processing transformer, 2021 a

    2021

  3. [11]

    Masked image training for generalizable deep image denoising

    Haoyu Chen, Jinjin Gu, Yihao Liu, Salma Abdel Magid, Chao Dong, Qiong Wang, Hanspeter Pfister, and Lei Zhu. Masked image training for generalizable deep image denoising. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 1692--1703, 2023 a

    2023

  4. [12]

    Hinet: Half instance normalization network for image restoration

    Liangyu Chen, Xin Lu, Jie Zhang, Xiaojie Chu, and Chengpeng Chen. Hinet: Half instance normalization network for image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 182--192, 2021 b

    2021

  5. [13]

    Simple baselines for image restoration

    Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, and Jian Sun. Simple baselines for image restoration. In Shai Avidan, Gabriel Brostow, Moustapha Ciss \'e , Giovanni Maria Farinella, and Tal Hassner (eds.), Computer Vision -- ECCV 2022, pp.\ 17--33, Cham, 2022. Springer Nature Switze...

    2022

  6. [14]

    Generative pretraining from pixels

    Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, and Ilya Sutskever. Generative pretraining from pixels. In International conference on machine learning, pp.\ 1691--1703. PMLR, 2020 a

    2020

  7. [15]

    A simple framework for contrastive learning of visual representations

    Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pp.\ 1597--1607. PMLR, 2020 b

    2020

  8. [16]

    Activating more pixels in image super-resolution transformer

    Xiangyu Chen, Xintao Wang, Jiantao Zhou, Yu Qiao, and Chao Dong. Activating more pixels in image super-resolution transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.\ 22367--22377, June 2023 b

    2023

  9. [17]

    An empirical study of training self-supervised vision transformers

    Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self-supervised vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pp.\ 9640--9649, 2021 c

    2021

  10. [18]

    Selective frequency network for image restoration

    Yuning Cui, Yi Tao, Zhenshan Bing, Wenqi Ren, Xinwei Gao, Xiaochun Cao, Kai Huang, and Alois Knoll. Selective frequency network for image restoration. In The Eleventh International Conference on Learning Representations, 2022

    2022

  11. [19]

    Image denoising by sparse 3-D transform-domain collaborative filtering

    Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian. Image denoising by sparse 3-D transform-domain collaborative filtering. TIP, 2007

    2007

  12. [20]

    Bert: Pre-training of deep bidirectional transformers for language understanding

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In North American Chapter of the Association for Computational Linguistics, 2019. URL https://api.semanticscholar.org/CorpusID:52967399

    2019

  13. [21]

    Taming transformers for high-resolution image synthesis

    Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 12873--12883, 2021

    2021

  14. [22]

    A general decoupled learning framework for parameterized image operators

    Qingnan Fan, Dongdong Chen, Lu Yuan, Gang Hua, Nenghai Yu, and Baoquan Chen. A general decoupled learning framework for parameterized image operators. TPAMI, 2019

    2019

  15. [23]

    Kodak lossless true color image suite, 2013

    Rich Franzen. Kodak lossless true color image suite, 2013. URL https://r0k.us/graphics/kodak/

    2013

  16. [24]

    Mcmae: Masked convolution meets masked autoencoders

    Peng Gao, Teli Ma, Hongsheng Li, Ziyi Lin, Jifeng Dai, and Yu Qiao. Mcmae: Masked convolution meets masked autoencoders. Advances in Neural Information Processing Systems, 35: 0 35632--35644, 2022

    2022

  17. [25]

    Content-noise complementary learning for medical image denoising

    Mufeng Geng, Xiangxi Meng, Jiangyuan Yu, Lei Zhu, Lujia Jin, Zhe Jiang, Bin Qiu, Hui Li, Hanjing Kong, Jianmin Yuan, et al. Content-noise complementary learning for medical image denoising. IEEE transactions on medical imaging, 41 0 (2): 0 407--419, 2021

    2021

  18. [26]

    Bootstrap your own latent-a new approach to self-supervised learning

    Jean-Bastien Grill, Florian Strub, Florent Altch \'e , Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neur...

    2020

  19. [27]

    Onerestore: A universal restoration framework for composite degradation

    Yu Guo, Yuan Gao, Yuxu Lu, Huilin Zhu, Ryan Wen Liu, and Shengfeng He. Onerestore: A universal restoration framework for composite degradation. In European Conference on Computer Vision, pp.\ 255--272. Springer, 2025

    2025

  20. [28]

    Revcolv2: Exploring disentangled representations in masked image modeling

    Qi Han, Yuxuan Cai, and Xiangyu Zhang. Revcolv2: Exploring disentangled representations in masked image modeling. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. URL https://openreview.net/forum?id=VvnfMeC3gQ

    2023

  21. [29]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\ 770--778, 2016

    2016

  22. [30]

    Momentum contrast for unsupervised visual representation learning

    Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 9729--9738, 2020

    2020

  23. [31]

    Masked autoencoders are scalable vision learners

    Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Doll \'a r, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 16000--16009, 2022

    2022

  24. [32]

    High-performance temporal reversible spiking neural networks with O (l) training memory and O (1) inference cost

    Jiakui Hu, Man Yao, Xuerui Qiu, Yuhong Chou, Yuxuan Cai, Ning Qiao, Yonghong Tian, Bo Xu, and Guoqi Li. High-performance temporal reversible spiking neural networks with O (l) training memory and O (1) inference cost. In Ruslan Salakhutdinov, Zico Kolter, Katherine Heller, Adr...

    2024

  25. [33]

    Single image super-resolution from transformed self-exemplars

    Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja. Single image super-resolution from transformed self-exemplars. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.\ 5197--5206, 2015. doi:10.1109/CVPR.2015.7299156

    2015

  26. [34]

    Batch normalization: Accelerating deep network training by reducing internal covariate shift

    Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pp.\ 448--456. pmlr, 2015

    2015

  27. [35]

    Frequency consistent adaptation for real world super resolution

    Xiaozhong Ji, Guangpin Tao, Yun Cao, Ying Tai, Tong Lu, Chengjie Wang, Jilin Li, and Feiyue Huang. Frequency consistent adaptation for real world super resolution. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pp.\ 1664--1672, 2021

    2021

  28. [36]

    One-pot multi-frame denoising

    Lujia Jin, Qing Guo, Shi Zhao, Lei Zhu, Qian Chen, Qiushi Ren, and Yanye Lu. One-pot multi-frame denoising. International Journal of Computer Vision, 132 0 (2): 0 515--536, 2024

    2024

  29. [37]

    Adam: A method for stochastic optimization

    Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014

    2014 arXiv

  30. [38]

    Auto-encoding variational bayes

    Diederik P Kingma and Max Welling. Auto-encoding variational bayes. In International Conference on Learning Representations, 2013

    2013

  31. [39]

    Deep learning

    Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. nature, 521 0 (7553): 0 436--444, 2015

    2015

  32. [40]

    Benchmarking single-image dehazing and beyond

    Boyi Li, Wenqi Ren, Dengpan Fu, Dacheng Tao, Dan Feng, Wenjun Zeng, and Zhangyang Wang. Benchmarking single-image dehazing and beyond. TIP, 2018

    2018

  33. [41]

    All-in-one image restoration for unknown corruption

    Boyun Li, Xiao Liu, Peng Hu, Zhongqin Wu, Jiancheng Lv, and Xi Peng. All-in-one image restoration for unknown corruption. In CVPR, 2022

    2022

  34. [42]

    An underwater image enhancement benchmark dataset and beyond

    Chongyi Li, Chunle Guo, Wenqi Ren, Runmin Cong, Junhui Hou, Sam Kwong, and Dacheng Tao. An underwater image enhancement benchmark dataset and beyond. IEEE transactions on image processing, 29: 0 4376--4389, 2019

    2019

  35. [43]

    All in one bad weather removal using architectural search

    Ruoteng Li, Robby T Tan, and Loong-Fah Cheong. All in one bad weather removal using architectural search. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 3175--3185, 2020

    2020

  36. [44]

    On efficient transformer-based image pre-training for low-level vision

    Wenbo Li, Xin Lu, Shengju Qian, and Jiangbo Lu. On efficient transformer-based image pre-training for low-level vision. In Edith Elkind (ed.), Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23 , pp.\ 1089--1097. International ...

    2023 doi

  37. [45]

    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, et al. Lsdir: A large scale dataset for image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 1775--1787, 2023 b

    2023

  38. [46]

    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 Proceedings of the IEEE/CVF international conference on computer vision, pp.\ 1833--1844, 2021

    2021

  39. [47]

    Enhanced deep residual networks for single image super-resolution

    Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. Enhanced deep residual networks for single image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp.\ 136--144, 2017

    2017

  40. [48]

    Focal loss for dense object detection

    Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Doll \'a r. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pp.\ 2980--2988, 2017

    2017

  41. [49]

    Degae: A new pretraining paradigm for low-level vision

    Yihao Liu, Jingwen He, Jinjin Gu, Xiangtao Kong, Yu Qiao, and Chao Dong. Degae: A new pretraining paradigm for low-level vision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.\ 23292--23303, June 2023 a

    2023

  42. [50]

    Discovering distinctive ``semantics'' in super-resolution networks, 2023 b

    Yihao Liu, Anran Liu, Jinjin Gu, Zhipeng Zhang, Wenhao Wu, Yu Qiao, and Chao Dong. Discovering distinctive ``semantics'' in super-resolution networks, 2023 b . URL https://openreview.net/forum?id=RrO3xNCqz7J

    2023

  43. [51]

    Desnownet: Context-aware deep network for snow removal

    Yun-Fu Liu, Da-Wei Jaw, Shih-Chia Huang, and Jenq-Neng Hwang. Desnownet: Context-aware deep network for snow removal. IEEE Transactions on Image Processing, 27 0 (6): 0 3064--3073, 2018

    2018

  44. [52]

    A convnet for the 2020s

    Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 11976--11986, 2022

    2022

  45. [53]

    Diffusion hyperfeatures: Searching through time and space for semantic correspondence

    Grace Luo, Lisa Dunlap, Dong Huk Park, Aleksander Holynski, and Trevor Darrell. Diffusion hyperfeatures: Searching through time and space for semantic correspondence. In Advances in Neural Information Processing Systems, 2023 a

    2023

  46. [54]

    o lund, and Thomas B Sch \

    Ziwei Luo, Fredrik K Gustafsson, Zheng Zhao, Jens Sj \"o lund, and Thomas B Sch \"o n. Controlling vision-language models for universal image restoration. In The Twelfth International Conference on Learning Representations, 2023 b

    2023

  47. [55]

    Structure-preserving super resolution with gradient guidance

    Cheng Ma, Yongming Rao, Yean Cheng, Ce Chen, Jiwen Lu, and Jie Zhou. Structure-preserving super resolution with gradient guidance. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 7769--7778, 2020

    2020

  48. [56]

    Waterloo exploration database: New challenges for image quality assessment models

    Kede Ma, Zhengfang Duanmu, Qingbo Wu, Zhou Wang, Hongwei Yong, Hongliang Li, and Lei Zhang. Waterloo exploration database: New challenges for image quality assessment models. IEEE Transactions on Image Processing, 26 0 (2): 0 1004--1016, 2016

    2016

  49. [57]

    Conde, Gregor Geigle

    Radu Timofte Marcos V. Conde, Gregor Geigle. High-quality image restoration following human instructions, 2024

    2024

  50. [58]

    A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

    David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV, 2001 a

    2001

  51. [59]

    A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

    David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proceedings Eighth IEEE International Conference on Computer Vision. IC...

    2001

  52. [60]

    Image super-resolution with non-local sparse attention

    Yiqun Mei, Yuchen Fan, and Yuqian Zhou. Image super-resolution with non-local sparse attention. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.\ 3517--3526, June 2021

    2021

  53. [61]

    Deep multi-scale convolutional neural network for dynamic scene deblurring

    Seungjun Nah, Tae Hyun Kim, and Kyoung Mu Lee. Deep multi-scale convolutional neural network for dynamic scene deblurring. In CVPR, 2017

    2017

  54. [62]

    Single image super-resolution via a holistic attention network

    Ben Niu, Weilei Wen, Wenqi Ren, Xiangde Zhang, Lianping Yang, Shuzhen Wang, Kaihao Zhang, Xiaochun Cao, and Haifeng Shen. Single image super-resolution via a holistic attention network. In Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, ...

    2020

  55. [63]

    Representation learning with contrastive predictive coding

    Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018

    2018 arXiv

  56. [64]

    Promptir: Prompting for all-in-one image restoration

    Vaishnav Potlapalli, Syed Waqas Zamir, Salman H Khan, and Fahad Shahbaz Khan. Promptir: Prompting for all-in-one image restoration. NeurIPS, 2023

    2023

  57. [65]

    Language models are unsupervised multitask learners

    Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1 0 (8): 0 9, 2019

    2019

  58. [66]

    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, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pp...

    2021

  59. [67]

    Designing bert for convolutional networks: Sparse and hierarchical masked modeling

    Keyu Tian, Yi Jiang, Chen Lin, Liwei Wang, Zehuan Yuan, et al. Designing bert for convolutional networks: Sparse and hierarchical masked modeling. In The Eleventh International Conference on Learning Representations, 2022

    2022

  60. [68]

    Maxim: Multi-axis mlp for image processing

    Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, and Yinxiao Li. Maxim: Multi-axis mlp for image processing. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 5769--5780, 2022

    2022

  61. [69]

    Neural discrete representation learning

    Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. Advances in neural information processing systems, 30, 2017

    2017

  62. [70]

    Attention is all you need

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, ukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017

    2017

  63. [71]

    Promptrestorer: A prompting image restoration method with degradation perception

    Cong Wang, Jinshan Pan, Wei Wang, Jiangxin Dong, Mengzhu Wang, Yakun Ju, and Junyang Chen. Promptrestorer: A prompting image restoration method with degradation perception. Advances in Neural Information Processing Systems, 36, 2024

    2024

  64. [72]

    Unsupervised degradation representation learning for blind super-resolution

    Longguang Wang, Yingqian Wang, Xiaoyu Dong, Qingyu Xu, Jungang Yang, Wei An, and Yulan Guo. Unsupervised degradation representation learning for blind super-resolution. In CVPR, 2021

    2021

  65. [73]

    Understanding contrastive representation learning through alignment and uniformity on the hypersphere

    Tongzhou Wang and Phillip Isola. Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In International conference on machine learning, pp.\ 9929--9939. PMLR, 2020

    2020

  66. [74]

    Uformer: A general u-shaped transformer for image restoration

    Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu, and Houqiang Li. Uformer: A general u-shaped transformer for image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.\ 17683--17693, June 2022

    2022

  67. [75]

    Deep retinex decomposition for low-light enhancement

    Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Deep retinex decomposition for low-light enhancement. In British Machine Vision Conference, 2018

    2018

  68. [76]

    Simmim: A simple framework for masked image modeling

    Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu. Simmim: A simple framework for masked image modeling. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 9653--9663, 2022

    2022

  69. [77]

    Image super-resolution via sparse representation

    Jianchao Yang, John Wright, Thomas S Huang, and Yi Ma. Image super-resolution via sparse representation. IEEE transactions on image processing, 19 0 (11): 0 2861--2873, 2010

    2010

  70. [78]

    Deep joint rain detection and removal from a single image

    Wenhan Yang, Robby T Tan, Jiashi Feng, Jiaying Liu, Zongming Guo, and Shuicheng Yan. Deep joint rain detection and removal from a single image. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\ 1357--1366, 2017

    2017

  71. [79]

    Joint rain detection and removal from a single image with contextualized deep networks

    Wenhan Yang, Robby T Tan, Jiashi Feng, Zongming Guo, Shuicheng Yan, and Jiaying Liu. Joint rain detection and removal from a single image with contextualized deep networks. TPAMI, 2019

    2019

  72. [80]

    Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip

    Man Yao, Ole Richter, Guangshe Zhao, Ning Qiao, Yannan Xing, Dingheng Wang, Tianxiang Hu, Wei Fang, Tugba Demirci, Michele De Marchi, et al. Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip. Nature Communications, 15 0 (1): 0 4464, 2024

    2024

  73. [81]

    Scaling spike-driven transformer with efficient spike firing approximation training

    Man Yao, Xuerui Qiu, Tianxiang Hu, Jiakui Hu, Yuhong Chou, Keyu Tian, Jianxing Liao, Luziwei Leng, Bo Xu, and Guoqi Li. Scaling spike-driven transformer with efficient spike firing approximation training. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025

    2025

  74. [82]

    Efficient and explicit modelling of image hierarchies for image restoration

    Xiaoyu Xiang Yawei Li, Yuchen Fan, Denis Demandolx, Rakesh Ranjan, Radu Timofte, and Luc Van Gool. Efficient and explicit modelling of image hierarchies for image restoration. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2023

    2023

  75. [83]

    Restormer: Efficient transformer for high-resolution image restoration

    Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In CVPR, 2022

    2022

  76. [84]

    Ingredient-oriented multi-degradation learning for image restoration

    Jinghao Zhang, Jie Huang, Mingde Yao, Zizheng Yang, Hu Yu, Man Zhou, and Feng Zhao. Ingredient-oriented multi-degradation learning for image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 5825--5835, 2023

    2023

  77. [85]

    Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising

    Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang. Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Transactions on Image Processing, 26 0 (7): 0 3142--3155, 2017

    2017

  78. [86]

    Image super-resolution using very deep residual channel attention networks

    Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu. Image super-resolution using very deep residual channel attention networks. In ECCV, 2018

    2018

  79. [87]

    Selective hourglass mapping for universal image restoration based on diffusion model

    Dian Zheng, Xiao-Ming Wu, Shuzhou Yang, Jian Zhang, Jian-Fang Hu, and Wei-shi Zheng. Selective hourglass mapping for universal image restoration based on diffusion model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

    2024

  80. [88]

    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 gl...

  81. [89]

    @esa (Ref

    \@ifxundefined[1] #1\@undefined \@firstoftwo \@secondoftwo \@ifnum[1] #1 \@firstoftwo \@secondoftwo \@ifx[1] #1 \@firstoftwo \@secondoftwo [2] @ #1 \@temptokena #2 #1 @ \@temptokena \@ifclassloaded agu2001 natbib The agu2001 class already includes natbib coding, so you should ...

  82. [90]

    \@lbibitem[] @bibitem@first@sw\@secondoftwo \@lbibitem[#1]#2 \@extra@b@citeb \@ifundefined br@#2\@extra@b@citeb \@namedef br@#2 \@nameuse br@#2\@extra@b@citeb \@ifundefined b@#2\@extra@b@citeb @num @parse #2 @tmp #1 NAT@b@open@#2 NAT@b@shut@#2 \@ifnum @merge>\@ne @bibitem@firs...

  83. [91]

    @open @close @open @close and [1] URL: #1 \@ifundefined chapter * \@mkboth \@ifxundefined @sectionbib * \@mkboth * \@mkboth\@gobbletwo \@ifclassloaded amsart * \@ifclassloaded amsbook * \@ifxundefined @heading @heading NAT@ctr thebibliography [1] @ \@biblabel @NAT@ctr \@bibset...

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.