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REVIEW 5 major objections 5 minor 86 references

Consistent Diffusion: Denoising Diffusion Model with Data-Consistent Training for Image Restoration

T0 review · 5 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The paper claims that training a diffusion restoration model on its own backward-process inputs, instead of one-step forward-corrupted inputs, makes the cumulative test-time error the direct training objective.

desk verdict A simple, empirically effective training trick that is over-theorized: the proof of zero cumulative error is a definitional tautology, but the method itself deserves attention. read the letter →

arxiv 2412.12550 v1 pith:WH7O3U6B submitted 2024-12-17 cs.CV

Xinlong Cheng , Tiantian Cao , Guoan Cheng , Bangxuan Huang , Xinghan Tian , Ye Wang , Xiaoyu He , Weixin Li
show 2 more authors
Tianfan Xue Xuan Dong
This is my paper · ORCID
classification cs.CV
keywords imagerestorationdenoisingdiffusionmodelsdata-consistenttrainingcumulativeerrormodularpropagationsuper-resolution
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

Denoising diffusion restoration models are trained on inputs produced by a one-step forward corruption, but at test time they are fed inputs produced by their own multi-step backward process. The paper argues that this mismatch lets small per-step errors accumulate, producing the shape and color distortions seen in diffusion-based restoration. It proposes data-consistent training: generate the training input for step $t$ by running the backward process, exactly as testing does, so the input cumulative error term becomes zero by construction and the standard per-step loss directly optimizes the error that accumulates at test time. Using ResShift as the backbone, the paper reports higher accuracy than previous restoration models on five tasks: super-resolution, denoising, deraining, dehazing, and dual-camera super-resolution. It also provides an efficient two-step approximation that trades a small accuracy loss for training cost close to the baseline.

What carries the argument

The load-bearing object is the backward-process sample $x^{\mathrm{back}}_t$, the input that the model's own sequential inverse process would produce at step $t$ during testing. Training on this sample instead of the one-step forward-corrupted sample $x^{\mathrm{forw}}_t$ makes the input cumulative error in Eq. (6) identically zero, so the recursion in Eq. (2) collapses to $\hat{\xi}^{\mathrm{cumu}}_t = \hat{\xi}^{\mathrm{mod}}_t$ (Eq. (7)); the ordinary per-step loss then minimizes the cumulative error. The efficient version keeps the same idea but replaces the full backward rollout with one forward step plus one backward step and amplifies the resulting one-step error by $\lambda = T - t$ to approximate the accumulated error.

What would settle it

At test time on a data-consistently trained model, record $D(x_t^{\mathrm{back}}, x_t^{\mathrm{train}})$ and the per-step modular error simultaneously; if total output error keeps growing even where the recorded distance is zero and modular errors are small, the recursion in Eq. (7) has left out a real source of error accumulation.

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Extended reading notes

Core claim

The paper argues that the train/test data inconsistency, not the network architecture, is the main source of the accumulated error that distorts diffusion-based restoration. In traditional training the loss minimizes the modular error $\xi^{\mathrm{mod}}_t = D(f_\theta(x^{\mathrm{forw}}_t),\mathrm{GT}_t)$, but testing runs the whole chain so the real error is the cumulative error $\xi^{\mathrm{cumu}}_t = \xi^{\mathrm{mod}}_t + \mu_t \xi^{\mathrm{cumu}}_{t+1}$, with $\mu_t \ge 1$. By using the backward-process input $x^{\mathrm{back}}_t$ as $x^{\mathrm{train}}_t$, the paper obtains $D(x^{\mathrm{back}}_t,x^{\mathrm{train}}_t)=0$, making the cumulative error equal to the modular error, so the ordinary loss becomes a direct optimizer of the test-time error. The paper reports experiments on five restoration tasks with ResShift, and an ablation with latent diffusion, to demonstrate that claim.

Load-bearing premise

The whole scheme rests on the definition that the only way past errors enter a step is the distance between the backward-generated input and the training input; if mistakes can carry over through any other channel, setting that distance to zero does not make cumulative error equal modular error.

Editorial extensions

If this is right

  • If the central claim is correct, data-consistent training should transfer to any diffusion restoration backbone, and the paper's ablation shows it helps both ResShift and LDM.
  • Because the change is to training inputs rather than to network architecture or sampling, test-time inference cost stays exactly the same as the original DDM.
  • The efficient variant gives most of the benefit at near-baseline training cost: 19.8 GB and 28 h versus 16.5 GB and 27 h for ResShift, compared with 41.7 GB and 90 h for the full version.
  • The method is positioned as a general fix for the shape and color distortions that come from error accumulation, so it should apply to other iterative restoration pipelines without per-task architectural changes.

Reading between the lines

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

  • A testable extension the authors leave implicit: the zeroing argument is tied to their chosen definition of cumulative error, so applying data-consistent training under an error recursion that includes a separate stochastic-transition term would show whether the gains come specifically from closing the input gap.
  • Because the backward inputs used for training are produced by the current weights, early training epochs see low-quality trajectories; a warm-start schedule that begins with forward-process training before switching to data-consistent training is a natural refinement the paper does not test.
  • The efficient version's choice $\lambda = T - t$ assumes the one-step error grows linearly with the remaining number of steps; a per-task adaptive schedule for $\lambda$ could recover part of the gap between the efficient and full versions.
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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

5 major / 5 minor

Summary. The paper proposes a training methodology for denoising diffusion models (DDMs) in image restoration, called data-consistent training. The key idea is to replace the forward-process training input x_forw_t with the backward-process sample x_back_t at each diffusion step, so that the training distribution matches the testing distribution. The authors argue that this makes the input cumulative error zero by construction (Eq. 6), so that the training loss directly optimizes the cumulative error (Eq. 7). They instantiate the method on the ResShift backbone and report state-of-the-art or competitive PSNR/SSIM/LPIPS/FID/CLIPIQA numbers on five restoration tasks: super-resolution, denoising, deraining, dehazing, and dual-camera super-resolution. An efficient variant that amplifies a one-step error by a factor of T-t is also introduced. The paper includes an ablation with a latent diffusion model backbone and memory/time comparisons.

Significance. If the central claim is correct, the contribution is attractive: a simple, backbone-agnostic training recipe that reduces the train/test input discrepancy in iterative restoration models, with consistent gains across five tasks and both ResShift and LDM backbones. The paper is also honest in reporting an efficient version with reduced memory and computation, and it includes a useful computational-cost table. However, the theoretical justification is currently definitional: Eq. (6) sets the input cumulative error to zero by choosing the training input equal to the backward input, and the recursion in Eq. (2) is imported from prior work without verification for the ResShift backbone. The empirical evaluation lacks error bars or significance tests, and the denoising protocol has a train/test degradation mismatch. These issues are load-bearing for the paper's claimed guarantee, though the empirical recipe may still be salvageable.

major comments (5)
  1. [Sec. 4.1, Eqs. (5)-(7)] The derivation that data-consistent training removes cumulative error is a definitional identity rather than a substantive result. Eq. (6) states that the input cumulative error is D(x_back_t, x_train_t); since x_train_t is set to x_back_t, this quantity is necessarily zero. Eq. (7) then equates cumulative error with the modular error. This does not prove that no accumulated error remains in the backward trajectory; it only shows that the paper's chosen error recursion returns zero when its two arguments are identical. The manuscript needs to establish that the recursion in Eq. (2) is a faithful description of how errors propagate through the specific backbone and schedule, and that a zero value of the input-distance term accounts for all error contributed by previous iterations. Without that, the central theoretical claim is vacuous.
  2. [Sec. 3.1, Eqs. (2)-(3)] The cumulative-error recursion is stated as 'proved in [27]' and is imported without verification for ResShift. ResShift's backward process is not the same diffusion formulation analyzed in [27], and the paper does not show that the recursion holds for it. A backward input x_back_t is produced by repeated evaluations of the same network f_theta, so errors from earlier iterations can interact nonlinearly with the step-t mapping; the distance D(x_back_t, x_t) between two inputs does not, in general, determine how those earlier errors propagate. The authors should either re-derive the recursion under ResShift's specific update, state the necessary Lipschitz/contraction conditions, or provide an independent numerical test that Eq. (2) actually accounts for propagated error.
  3. [Sec. 5.1 and Tables 1-5] The denoising setup is inconsistent: training uses SIDD, while testing uses Urban100 with synthetic Gaussian noise of standard deviation 50. SIDD is a real-smartphone-noise dataset, so the train-time degradation distribution does not match the test-time Gaussian corruption; this mismatch can disadvantage task-specific denoisers and makes the comparison hard to interpret. Additionally, no error bars, repeated runs, or significance tests are reported. Given the large reported PSNR gains (e.g., Table 1, 32.25 dB vs. 30.81 dB for MAXIM; Table 2, 34.22 dB vs. 33.91 dB for DA-CLIP), the authors should report variance across at least three training runs or perform paired tests on the test images to confirm that the improvements are not within run-to-run noise.
  4. [Sec. 4.2, Eqs. (9)-(11)] The efficient variant depends on two hand-set quantities: it assumes mu_t = 1 in Eq. (2) and sets the amplification factor lambda = T - t. No derivation is given for either choice, and the statement that this ensures 'the strength of error is the same with that in consistent diffusion' is not substantiated. The ablation in Table 7 only compares no amplification, random amplification, and the proposed lambda = T - t; it does not sweep lambda, so the sensitivity of the efficient variant to this assumed propagation coefficient is unknown.
  5. [Table 6 and Sec. 5.2] The ablation results are internally inconsistent with the main-table narrative in a way that undercuts the claim that the proposed ResShift variant is the best. In Table 6, 'LDM+DCT' achieves 27.60 dB on RealSR, which is higher than 'ResShift+DCT (Ours)' at 25.96 dB from Table 5. Moreover, LDM improves from 25.19 to 27.60 dB (+2.41 dB) under DCT, while ResShift improves only from 24.59 to 25.96 dB (+1.37 dB). This does not by itself invalidate the method, but it complicates the claim that the proposed training is uniformly beneficial and demands an explanation, especially since the main tables highlight only the ResShift-based results.
minor comments (5)
  1. [References] Reference [33] for the SIDD dataset is incorrect: it cites a paper about detecting sensitive data exfiltration, not the Single Image Denoising Dataset of Abdelhamed et al. The correct dataset citation should be provided.
  2. [Sec. 2.1 and Sec. 5.1] There are several typos: 'miles-stone', 'specfically', and 'Compasion Methods' should read 'milestone', 'specifically', and 'Comparison Methods'; the backbone name is also inconsistently spelled as both 'Resshift' and 'ResShift'.
  3. [Figs. 1 and 3, Sec. 5.1] Figures 1 and 3 show KL divergence on the y-axis, while Sec. 5.1 states that mean squared error is used as the loss metric beta_t. Please clarify whether the modular and cumulative error plots are computed with KL divergence, MSE, or another metric, since the paper's error definitions in Eq. (1)-(3) use a generic 'D'.
  4. [Sec. 3.1, Eq. (3)] The indexing in Eq. (3) is confusing: the left-hand side is xi_cumu_{t+1}, but the right-hand side is D(x_back_t, x_t), where both arguments are at the same time index t. Please realign the index convention so that the input error that propagates from iteration t+1 to iteration t is unambiguously defined.
  5. [Table 1] The caption of Table 1 says 'We compare 8 general and 4 task-specific restoration models,' but the table lists 12 baseline methods plus Ours; please recount or reclassify the comparison methods.

Circularity Check

1 steps flagged · score 6.0 of 10

The proof that data-consistent training zeroes the cumulative error is definitional: Eq. (6) sets x_train = x_back and thereby makes the defined quantity D(x_back, x_train) vanish, so Eq. (7) is an identity rather than a derived error-propagation result.

  1. self definitional [Section 4.1, Eqs. (6)-(7), following the definition of input cumulative error in Section 3.1, Eq. (3)]
    "according to Eq. 3, the input cumulative error changes to ξ^cumu_{t+1} = D(x_back_t, x_train_t) = D(x_back_t, x_back_t) = 0. (6) Thus, according to Eq. 2, the cumulative error is ξ^cumu_t = ξ^mod_t + μ_t · 0 = ξ^mod_t. (7) This indicates that by using x_back_t as the input, the cumulative error at iteration t is only decided by ξ^mod_t and there does not exist any other error sources."

    The 'input cumulative error' was defined in Eq. (3) as D(x_back_t, x_t). The method then selects x_train_t = x_back_t, so Eq. (6) obtains D(x_back_t, x_back_t) = 0 by substitution into the definition, not by measurement or by analyzing the model's dynamics. Eq. (7) then concludes that the total cumulative error at step t is exactly the modular error, because the only other term, μ_t times that zero, vanishes. This is a definitional identity, not a theorem about how errors from earlier backward-process steps actually propagate through the ResShift backbone.

full rationale

The paper contains one significant definitional circularity, located in the theoretical justification of data-consistent training. In Section 3.1, the 'input cumulative error' is defined as D(x_back_t, x_t) (Eq. 3) and the total cumulative error is decomposed as ξ_mod_t + μ_t ξ_cumu_{t+1} (Eq. 2). In Section 4.1, the method chooses x_train_t = x_back_t, so Eq. (6) becomes D(x_back_t, x_back_t) = 0 by construction. Eq. (7) then concludes ξ_cumu_t = ξ_mod_t. This is a substitution into the definition rather than a derivation from the dynamics of the ResShift backbone; it does not establish that there are no other accumulated error sources in the actual backward trajectory. The framework (Eqs. 2-3, μ_t ≥ 1) is imported from [27] and is not re-derived or empirically verified for ResShift here. The empirical section is self-contained and substantial: the paper reports PSNR/SSIM/LPIPS/FID/CLIPIQA on five restoration tasks and includes ablations, all of which are external evidence and do not depend on the definitional step. There is no load-bearing self-citation; the cited reference [27] is not authored by the present paper's authors. Because the central analytical claim 'the loss directly optimizes cumulative error' reduces by construction to the chosen training input, the score is 6 rather than 0-2; it is not 8 or 10 because the method itself is a genuine intervention and its experimental evaluation stands independently of the definitional identity.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new physical quantities or entities. The free parameters are the amplification factor λ(t) and the assumed propagation coefficient μ_t=1, both used in the efficient version. The central proof leans on definitions and bounds inherited from [27] without independent verification, which is the main source of circularity burden.

free parameters (2)
  • amplification factor λ(t) = λ = T - t
    In the efficient version (Eq. 10), the one-step error e is multiplied by λ = T - t to approximate the cumulative error over the remaining steps. The choice is hand-selected and ablated against 'No amplification' and 'Random amplification' (Table 7), but no principled derivation or sensitivity analysis is given.
  • assumed propagation coefficient μ_t = μ_t = 1 for all t (efficient version)
    The efficient version assumes μ_t = 1 in Eq. (2) to justify the amplification schedule; the paper relies on [27] for μ_t ≥ 1 but chooses exactly 1 for the design, a fitted modeling choice.
assumptions (3)
  • domain assumption Cumulative error recurrence (Eq. 2) and input error definition (Eq. 3) from [27] hold for ResShift
    The paper cites [27] for these definitions and uses them to prove that DCT eliminates input cumulative error; it does not verify the recurrence for the specific backbone or measure μ_t.
  • domain assumption μ_t ≥ 1 as proved in [27]
    This lower bound is used to argue that cumulative errors grow during testing and cannot be ignored (Section 3.2); it is not re-derived or measured.
  • domain assumption The network f_θ trained with MSE on self-generated trajectories learns to correct its own errors
    The success of DCT presupposes that the model can be trained to recover ground truth from inputs that are its own imperfect backward-chain outputs; this is a standard learning assumption but is not formally guaranteed.

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Cite this review

Pith. "Pith review of Consistent Diffusion: Denoising Diffusion Model with Data-Consistent Training for Image Restoration." pith.science (2026). https://pith.science/paper/WH7O3U6B

@misc{pith2026241212550,
  author       = {Pith},
  title        = {Pith review of: Consistent Diffusion: Denoising Diffusion Model with Data-Consistent Training for Image Restoration},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WH7O3U6B}},
  note         = {Machine review of arXiv:2412.12550}
}
read the original abstract

In this work, we address the limitations of denoising diffusion models (DDMs) in image restoration tasks, particularly the shape and color distortions that can compromise image quality. While DDMs have demonstrated a promising performance in many applications such as text-to-image synthesis, their effectiveness in image restoration is often hindered by shape and color distortions. We observe that these issues arise from inconsistencies between the training and testing data used by DDMs. Based on our observation, we propose a novel training method, named data-consistent training, which allows the DDMs to access images with accumulated errors during training, thereby ensuring the model to learn to correct these errors. Experimental results show that, across five image restoration tasks, our method has significant improvements over state-of-the-art methods while effectively minimizing distortions and preserving image fidelity.

Figures

Figures reproduced from arXiv: 2412.12550 by the authors.

Figure 1
Figure 1. (a) shows PSNR values of the best comparison method [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Pipelines of the training methods of traditional DDM vs. our consistent diffusion. At iteration [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. (a) shows the statistics of traditional DDM across five tasks, revealing a persistent gap between the modular error [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: In traditional DDM, the gap of training and testing in (a) [PITH_FULL_IMAGE:figures/full_fig_p003_4.png]
Figure 5
Figure 5. Figure 5: Example results in single image super-resolution on RealSR. The marked regions are enlarged. Due to page limits, we show [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Example results in single image super-resolution on CameraFusion. Please see more results in the supplementary materials. [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 7
Figure 7. Figure 7: Example results in dual-camera super resolution on CameraFusion. Please see more results in supplementary materials. [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: Example results in image denoising on Urban100. Please see more results in supplementary materials. [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]
Figure 9
Figure 9. Figure 9: Example results in image deraining on Rain-100H. Please see more results in supplementary materials. [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]
Figure 10
Figure 10. Figure 10: Example results in image dehaze on RESIDE-6k. Please see more results in supplementary materials. [PITH_FULL_IMAGE:figures/full_fig_p008_10.png]
Figure 11
Figure 11. Figure 11: Example results of the ablation study about our data-consistent training. ‘DCT’ is short for the proposed data-consistent training. [PITH_FULL_IMAGE:figures/full_fig_p008_11.png]

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Reference graph

Works this paper leans on

86 extracted references · 61 canonical work pages

  1. [27]

    On error prop- agation of diffusion models

    Yangming Li and Mihaela van der Schaar. On error prop- agation of diffusion models. In The Twelfth International Conference on Learning Representations, 2023. 1, 3, 7, 8

    work page 2023

  2. [1]

    Defocus de- blurring using dual-pixel data

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

    2020

  3. [2]

    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 Pro- ceedings of the IEEE conference on computer vision and pat- tern recognition workshops, pages 126–135, 2017. 5

    2017

  4. [3]

    Pre-trained image processing transformer

    Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yip- ing Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao. Pre-trained image processing transformer. In Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 12299–12310, 2021. 2, 4, 5

    2021

  5. [4]

    Hat: Hybrid attention transformer for image restoration

    Xiangyu Chen, Xintao Wang, Wenlong Zhang, Xiangtao Kong, Yu Qiao, Jiantao Zhou, and Chao Dong. Hat: Hybrid attention transformer for image restoration. arXiv preprint arXiv:2309.05239, 2023. 7

    arXiv 2023

  6. [5]

    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. InProceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , pages 22367–22377, 2023. 2

    2023

  7. [6]

    Dea-net: Single image dehazing based on detail-enhanced convolution and content-guided attention

    Zixuan Chen, Zewei He, and Zhe-Ming Lu. Dea-net: Single image dehazing based on detail-enhanced convolution and content-guided attention. IEEE Transactions on Image Pro- cessing, 2024. 2

    2024

  8. [7]

    Irnext: Rethinking convolutional network de- sign for image restoration

    Yuning Cui, Wenqi Ren, Sining Yang, Xiaochun Cao, and Alois Knoll. Irnext: Rethinking convolutional network de- sign for image restoration. In International conference on machine learning, 2023. 2

    2023

Show all 86 references
  1. [8]

    Omni-kernel net- work for image restoration

    Yuning Cui, Wenqi Ren, and Alois Knoll. Omni-kernel net- work for image restoration. In Proceedings of the AAAI Con- ference on Artificial Intelligence, pages 1426–1434, 2024. 2

    2024

  2. [9]

    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. IEEE Transactions on image processing, 16(8):2080–2095, 2007. 2

    2007

  3. [10]

    Self-supervised non-uniform kernel estimation with flow-based motion prior for blind im- age deblurring

    Zhenxuan Fang, Fangfang Wu, Weisheng Dong, Xin Li, Jin- jian Wu, and Guangming Shi. Self-supervised non-uniform kernel estimation with flow-based motion prior for blind im- age deblurring. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , p...

    2023

  4. [11]

    Generative dif- fusion prior for unified image restoration and enhancement

    Ben Fei, Zhaoyang Lyu, Liang Pan, Junzhe Zhang, Weidong Yang, Tianyue Luo, Bo Zhang, and Bo Dai. Generative dif- fusion prior for unified image restoration and enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9935–9946, 2023. 2

    2023

  5. [12]

    Clearing the skies: A deep network archi- tecture for single-image rain removal

    Xueyang Fu, Jiabin Huang, Xinghao Ding, Yinghao Liao, and John Paisley. Clearing the skies: A deep network archi- tecture for single-image rain removal. IEEE Transactions on Image Processing, 26(6):2944–2956, 2017. 2

    2017

  6. [13]

    Removing rain from single images via a deep detail network

    Xueyang Fu, Jiabin Huang, Delu Zeng, Yue Huang, Xinghao Ding, and John Paisley. Removing rain from single images via a deep detail network. In Proceedings of the IEEE con- ference on computer vision and pattern recognition , pages 3855–3863, 2017. 2

    2017

  7. [14]

    Image dehazing transformer with transmission-aware 3d position embedding

    Chun-Le Guo, Qixin Yan, Saeed Anwar, Runmin Cong, Wenqi Ren, and Chongyi Li. Image dehazing transformer with transmission-aware 3d position embedding. InProceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5812–5820, 2022. 2

    2022

  8. [15]

    Single image haze removal using dark channel prior

    Kaiming He, Jian Sun, and Xiaoou Tang. Single image haze removal using dark channel prior. IEEE transactions on pat- tern analysis and machine intelligence , 33(12):2341–2353,

  9. [16]

    Denoising dif- fusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising dif- fusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020. 2

    2020

  10. [17]

    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 Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5197–5206, 2015. 4, 5

    2015

  11. [18]

    Real-world super-resolution via kernel estimation and noise injection

    Xiaozhong Ji, Yun Cao, Ying Tai, Chengjie Wang, Jilin Li, and Feiyue Huang. Real-world super-resolution via kernel estimation and noise injection. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 466–467, 2020. 5, 7

    2020

  12. [19]

    Multi-scale progressive fusion network for single image deraining

    Kui Jiang, Zhongyuan Wang, Peng Yi, Chen Chen, Baojin Huang, Yimin Luo, Jiayi Ma, and Junjun Jiang. Multi-scale progressive fusion network for single image deraining. In Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 8346–8355, 2020. 5

    2020

  13. [20]

    Reference-based image and video super- resolution via c 2-matching

    Yuming Jiang, Kelvin CK Chan, Xintao Wang, Chen Change Loy, and Ziwei Liu. Reference-based image and video super- resolution via c 2-matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(7):8874–8887, 2022. 6

    2022

  14. [21]

    Photo- realistic single image super-resolution using a generative ad- versarial network

    Christian Ledig, Lucas Theis, Ferenc Husz´ar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al. Photo- realistic single image super-resolution using a generative ad- versarial network. In Proceedings of the IE...

  15. [22]

    Local texture estima- tor for implicit representation function

    Jaewon Lee and Kyong Hwan Jin. Local texture estima- tor for implicit representation function. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1929–1938, 2022. 2

    1929

  16. [23]

    Universal denoising networks: a novel cnn architecture for image denoising

    Stamatios Lefkimmiatis. Universal denoising networks: a novel cnn architecture for image denoising. In Proceed- ings of the IEEE conference on computer vision and pattern recognition, pages 3204–3213, 2018. 2

    2018

  17. [24]

    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. IEEE Transactions on Image Processing, 28(1):492–505, 2018. 5

    2018

  18. [25]

    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 Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 17452– 17462, 2022. 4, 5, 7

    2022

  19. [26]

    Heavy rain image restoration: Integrating physics model and condi- tional adversarial learning

    Ruoteng Li, Loong-Fah Cheong, and Robby T Tan. Heavy rain image restoration: Integrating physics model and condi- tional adversarial learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 1633–1642, 2019. 2

    2019

  20. [28]

    Physically disentangled intra-and inter-domain adap- tation for varicolored haze removal

    Yi Li, Yi Chang, Yan Gao, Changfeng Yu, and Luxin Yan. Physically disentangled intra-and inter-domain adap- tation for varicolored haze removal. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5841–5850, 2022. 2

    2022

  21. [29]

    Effi- cient and explicit modelling of image hierarchies for image restoration

    Yawei Li, Yuchen Fan, Xiaoyu Xiang, Denis Demandolx, Rakesh Ranjan, Radu Timofte, and Luc Van Gool. Effi- cient and explicit modelling of image hierarchies for image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18278–...

    2023

  22. [30]

    Swinir: Image restoration us- ing swin transformer

    Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. Swinir: Image restoration us- ing swin transformer. InProceedings of the IEEE/CVF inter- national conference on computer vision , pages 1833–1844,

  23. [31]

    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 confer- ence on computer vision and pattern recognition workshops, pages 136–144, 2017. 2

    2017

  24. [32]

    Improving image restoration through removing degradations in textual repre- sentations

    Jingbo Lin, Zhilu Zhang, Yuxiang Wei, Dongwei Ren, Dong- sheng Jiang, Qi Tian, and Wangmeng Zuo. Improving image restoration through removing degradations in textual repre- sentations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages...

    2024

  25. [33]

    Sidd: A frame- work for detecting sensitive data exfiltration by an insider attack

    Yali Liu, Cherita Corbett, Ken Chiang, Rennie Archibald, Biswanath Mukherjee, and Dipak Ghosal. Sidd: A frame- work for detecting sensitive data exfiltration by an insider attack. In 2009 42nd Hawaii international conference on system sciences, pages 1–10. IEEE, 2009. 5

    2009

  26. [34]

    Diff-plugin: Revitalizing details for diffusion-based low-level tasks

    Yuhao Liu, Zhanghan Ke, Fang Liu, Nanxuan Zhao, and Rynson WH Lau. Diff-plugin: Revitalizing details for diffusion-based low-level tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4197–4208, 2024. 1, 2, 4, 5, 7

    2024

  27. [35]

    Masa-sr: Matching acceleration and spatial adaptation for reference-based image super-resolution

    Liying Lu, Wenbo Li, Xin Tao, Jiangbo Lu, and Jiaya Jia. Masa-sr: Matching acceleration and spatial adaptation for reference-based image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6368–6377, 2021. 6

    2021

  28. [36]

    Mixdehazenet: Mix structure block for image dehazing net- work

    LiPing Lu, Qian Xiong, Bingrong Xu, and Duanfeng Chu. Mixdehazenet: Mix structure block for image dehazing net- work. In 2024 International Joint Conference on Neural Net- works (IJCNN), pages 1–10. IEEE, 2024. 5

    2024

  29. [37]

    Photo-realistic image restoration in the wild with controlled vision-language models

    Ziwei Luo, Fredrik K Gustafsson, Zheng Zhao, Jens Sj¨olund, and Thomas B Sch ¨on. Photo-realistic image restoration in the wild with controlled vision-language models. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6641–6651, 2024...

    2024

  30. [38]

    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 Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3883–3891,

  31. [39]

    Revisiting non-autoregressive transformers for efficient im- age synthesis

    Zanlin Ni, Yulin Wang, Renping Zhou, Jiayi Guo, Jinyi Hu, Zhiyuan Liu, Shiji Song, Yuan Yao, and Gao Huang. Revisiting non-autoregressive transformers for efficient im- age synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages ...

    2024

  32. [40]

    Promptir: Prompting for all-in-one blind image restoration

    V Potlapalli, SW Zamir, S Khan, and FS Khan. Promptir: Prompting for all-in-one blind image restoration. arxiv 2023. arXiv preprint arXiv:2306.13090, 2023. 4, 5, 7

    2023 arXiv

  33. [41]

    Gated fusion network for single image dehazing

    Wenqi Ren, Lin Ma, Jiawei Zhang, Jinshan Pan, Xiaochun Cao, Wei Liu, and Ming-Hsuan Yang. Gated fusion network for single image dehazing. In Proceedings of the IEEE con- ference on computer vision and pattern recognition , pages 3253–3261, 2018. 2

    2018

  34. [42]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022. 2, 7

    2022

  35. [43]

    Photorealistic text-to-image diffusion models with deep language understanding

    Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in neural information...

    2022

  36. [44]

    Progressive distillation for fast sampling of diffusion models

    Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. arXiv preprint arXiv:2202.00512, 2022. 2

    2022 arXiv

  37. [45]

    Denoising diffusion implicit models

    Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 2

    2010 arXiv

  38. [46]

    Vision transformers for single image dehazing

    Yuda Song, Zhuqing He, Hui Qian, and Xin Du. Vision transformers for single image dehazing. IEEE Transactions on Image Processing, 32:1927–1941, 2023. 5

    1927

  39. [47]

    Sadnet: Semi-supervised sin- gle image dehazing method based on an attention mecha- nism

    Ziyi Sun, Yunfeng Zhang, Fangxun Bao, Ping Wang, Xunxi- ang Yao, and Caiming Zhang. Sadnet: Semi-supervised sin- gle image dehazing method based on an attention mecha- nism. ACM Transactions on Multimedia Computing, Com- munications, and Applications (TOMM) , 18(2):1–23, 2022. 4

    2022

  40. [48]

    Splatter image: Ultra-fast single-view 3d recon- struction

    Stanislaw Szymanowicz, Chrisitian Rupprecht, and Andrea Vedaldi. Splatter image: Ultra-fast single-view 3d recon- struction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 10208– 10217, 2024. 2

    2024

  41. [49]

    Stripformer: Strip transformer for fast image deblurring

    Fu-Jen Tsai, Yan-Tsung Peng, Yen-Yu Lin, Chung-Chi Tsai, and Chia-Wen Lin. Stripformer: Strip transformer for fast image deblurring. In European conference on computer vi- sion, pages 146–162. Springer, 2022. 2

    2022

  42. [50]

    Banet: a blur-aware attention network for dynamic scene deblurring

    Fu-Jen Tsai, Yan-Tsung Peng, Chung-Chi Tsai, Yen-Yu Lin, and Chia-Wen Lin. Banet: a blur-aware attention network for dynamic scene deblurring. IEEE Transactions on Image Processing, 31:6789–6799, 2022. 2

    2022

  43. [51]

    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, pages 5769–5780, 2022. 4, 5, 7

    2022

  44. [52]

    Transweather: Transformer-based restoration of im- ages degraded by adverse weather conditions

    Jeya Maria Jose Valanarasu, Rajeev Yasarla, and Vishal M Patel. Transweather: Transformer-based restoration of im- ages degraded by adverse weather conditions. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2353–2363, 2022. 2

    2022

  45. [53]

    Promptre- storer: A prompting image restoration method with degrada- tion perception

    Cong Wang, Jinshan Pan, Wei Wang, Jiangxin Dong, Mengzhu Wang, Yakun Ju, and Junyang Chen. Promptre- storer: A prompting image restoration method with degrada- tion perception. Advances in Neural Information Processing Systems, 36:8898–8912, 2023. 2

    2023

  46. [54]

    Exploiting diffusion prior for real-world image super-resolution

    Jianyi Wang, Zongsheng Yue, Shangchen Zhou, Kelvin CK Chan, and Chen Change Loy. Exploiting diffusion prior for real-world image super-resolution. International Journal of Computer Vision, pages 1–21, 2024. 2, 7

    2024

  47. [55]

    Dual-camera super-resolution with aligned attention modules

    Tengfei Wang, Jiaxin Xie, Wenxiu Sun, Qiong Yan, and Qifeng Chen. Dual-camera super-resolution with aligned attention modules. In Proceedings of the IEEE/CVF Inter- national Conference on Computer Vision, pages 2001–2010,

    2001

  48. [56]

    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 Proceedings of the IEEE/CVF inter- national conference on computer vision , pages 1905–1914,

    1905

  49. [57]

    Sinsr: diffusion-based image super- resolution in a single step

    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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ,...

    2024

  50. [58]

    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, pages 17683–17693, 2022. 2

    2022

  51. [59]

    Blind2unblind: Self-supervised image denoising with visible blind spots

    Zejin Wang, Jiazheng Liu, Guoqing Li, and Hua Han. Blind2unblind: Self-supervised image denoising with visible blind spots. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2027–2036,

    2027

  52. [60]

    De- blurring via stochastic refinement

    Jay Whang, Mauricio Delbracio, Hossein Talebi, Chitwan Saharia, Alexandros G Dimakis, and Peyman Milanfar. De- blurring via stochastic refinement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16293–16303, 2022. 2

    2022

  53. [61]

    Id-blau: Image deblurring by implicit diffusion-based reblurring augmentation

    Jia-Hao Wu, Fu-Jen Tsai, Yan-Tsung Peng, Chung-Chi Tsai, Chia-Wen Lin, and Yen-Yu Lin. Id-blau: Image deblurring by implicit diffusion-based reblurring augmentation. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 25847–25856, 2024. 2

    2024

  54. [62]

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

    Rongyuan Wu, Tao Yang, Lingchen Sun, Zhengqiang Zhang, Shuai Li, and Lei Zhang. Seesr: Towards semantics- aware real-world image super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 25456–25467, 2024. 2, 7

    2024

  55. [63]

    Diffir: Efficient diffusion model for image restoration

    Bin Xia, Yulun Zhang, Shiyin Wang, Yitong Wang, Xing- long Wu, Yapeng Tian, Wenming Yang, and Luc Van Gool. Diffir: Efficient diffusion model for image restoration. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13095–13105, 2023. 2, 7

    2023

  56. [64]

    Image de-raining transformer

    Jie Xiao, Xueyang Fu, Aiping Liu, Feng Wu, and Zheng-Jun Zha. Image de-raining transformer. IEEE Transactions on Pattern Analysis and Machine Intelligence , 45(11):12978– 12995, 2022. 2

    2022

  57. [65]

    Cur transformer: A convo- lutional unbiased regional transformer for image denoising

    Kang Xu, Weixin Li, Xia Wang, Xiaoyan Hu, Ke Yan, Xi- aojie Wang, and Xuan Dong. Cur transformer: A convo- lutional unbiased regional transformer for image denoising. ACM Transactions on Multimedia Computing, Communica- tions and Applications, 19(3):1–22, 2023. 2

    2023

  58. [66]

    Zero-shot dual-lens super-resolution

    Ruikang Xu, Mingde Yao, and Zhiwei Xiong. Zero-shot dual-lens super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 9130–9139, 2023. 6

    2023

  59. [67]

    Learning texture transformer network for image super-resolution

    Fuzhi Yang, Huan Yang, Jianlong Fu, Hongtao Lu, and Bain- ing Guo. Learning texture transformer network for image super-resolution. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , pages 5791–5800, 2020. 2, 6

    2020

  60. [68]

    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(11):2861–2873, 2010. 2

    2010

  61. [69]

    Csformer: Bridging convolution and transformer for compressive sensing.IEEE Transactions on Image Processing, 32:2827–2842, 2023

    Dongjie Ye, Zhangkai Ni, Hanli Wang, Jian Zhang, Shiqi Wang, and Sam Kwong. Csformer: Bridging convolution and transformer for compressive sensing.IEEE Transactions on Image Processing, 32:2827–2842, 2023. 2

    2023

  62. [70]

    Learning diffusion texture priors for image restoration

    Tian Ye, Sixiang Chen, Wenhao Chai, Zhaohu Xing, Jing Qin, Ge Lin, and Lei Zhu. Learning diffusion texture priors for image restoration. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 2524–2534, 2024. 2

    2024

  63. [71]

    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 Proceedings of the IEEE/CVF Conference on Computer Vision and...

    2024

  64. [72]

    Kedusr: Real-world dual-lens super-resolution via kernel-free match- ing

    Huanjing Yue, Zifan Cui, Kun Li, and Jingyu Yang. Kedusr: Real-world dual-lens super-resolution via kernel-free match- ing. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6881–6889, 2024. 2, 6

    2024

  65. [73]

    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. Advances in Neural Infor- mation Processing Systems, 36, 2024. 1, 2, 3, 7

    2024

  66. [74]

    Restormer: Efficient transformer for high-resolution image restoration

    Syed Waqas Zamir, Aditya Arora, Salman Khan, Mu- nawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5728–5739,

  67. [75]

    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 Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3836–3847, 2023. 2

    2023

  68. [76]

    Real-world image super-resolution as multi-task learning

    Wenlong Zhang, Xiaohui Li, Guangyuan Shi, Xiangyu Chen, Yu Qiao, Xiaoyun Zhang, Xiao-Ming Wu, and Chao Dong. Real-world image super-resolution as multi-task learning. Advances in Neural Information Processing Systems , 36,

  69. [77]

    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 Proceedings of the European conference on computer vision (ECCV), pages 286–301, 2018. 2

    2018

  70. [78]

    Kbnet: Kernel basis network for image restoration

    Yi Zhang, Dasong Li, Xiaoyu Shi, Dailan He, Kangning Song, Xiaogang Wang, Hongwei Qin, and Hongsheng Li. Kbnet: Kernel basis network for image restoration. arXiv preprint arXiv:2303.02881, 2023. 4, 5

    2023 arXiv

  71. [79]

    Im- age super-resolution by neural texture transfer

    Zhifei Zhang, Zhaowen Wang, Zhe Lin, and Hairong Qi. Im- age super-resolution by neural texture transfer. In Proceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7982–7991, 2019. 6

    2019

  72. [80]

    Self-supervised learning for real-world super-resolution from dual zoomed observations

    Zhilu Zhang, Ruohao Wang, Hongzhi Zhang, Yunjin Chen, and Wangmeng Zuo. Self-supervised learning for real-world super-resolution from dual zoomed observations. In Eu- ropean Conference on Computer Vision , pages 610–627. Springer, 2022. 2, 6

    2022

  73. [81]

    Uni-controlnet: All-in-one control to text-to-image diffusion models

    Shihao Zhao, Dongdong Chen, Yen-Chun Chen, Jianmin Bao, Shaozhe Hao, Lu Yuan, and Kwan-Yee K Wong. Uni-controlnet: All-in-one control to text-to-image diffusion models. Advances in Neural Information Processing Sys- tems, 36, 2024. 2

    2024

  74. [82]

    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 , pages 25445– 25455,...

    2024

  75. [83]

    Srformer: Permuted self-attention for single image super-resolution

    Yupeng Zhou, Zhen Li, Chun-Le Guo, Song Bai, Ming-Ming Cheng, and Qibin Hou. Srformer: Permuted self-attention for single image super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 12780–12791, 2023. 2, 7

    2023

  76. [84]

    Learn- ing weather-general and weather-specific features for image restoration under multiple adverse weather conditions

    Yurui Zhu, Tianyu Wang, Xueyang Fu, Xuanyu Yang, Xin Guo, Jifeng Dai, Yu Qiao, and Xiaowei Hu. Learn- ing weather-general and weather-specific features for image restoration under multiple adverse weather conditions. In Proceedings of the IEEE/CVF conference on computer vi- si...

    2023

  77. [85]

    Denoising dif- fusion models for plug-and-play image restoration

    Yuanzhi Zhu, Kai Zhang, Jingyun Liang, Jiezhang Cao, Bi- han Wen, Radu Timofte, and Luc Van Gool. Denoising dif- fusion models for plug-and-play image restoration. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1219–1229, 2023. 2

    2023

  78. [86]

    Flowie: Efficient image enhancement via rectified flow

    Yixuan Zhu, Wenliang Zhao, Ao Li, Yansong Tang, Jie Zhou, and Jiwen Lu. Flowie: Efficient image enhancement via rectified flow. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 13–22, 2024. 2

    2024

Pith tools

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