Pith. sign in

REVIEW 5 major objections 5 minor 103 references

Generative Latent Kernel Modeling for Blind Motion Deblurring

T0 review · 5 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read The paper claims that a pretrained GAN-based kernel prior, combined with a learned kernel initializer, confines blind motion deblurring to a compact latent kernel manifold, stabilizes kernel estimation, and improves several existing…

desk verdict A credible plug-and-play extension of the authors' ECCV kernel-prior work, with broad experiments, but the central claim that the manifold constraint rather than the learned initializer drives the gains is not actually tested. read the letter →

arxiv 2507.09285 v1 pith:DHGK7THA submitted 2025-07-12 cs.CV

Chenhao Ding , Jiangtao Zhang , Zongsheng Yue , Hui Wang , Qian Zhao , Deyu Meng This is my paper · ORCID
classification cs.CV
keywords blindmotiondeblurringgenerativekernelpriorinitializerlatentmanifolddeepnon-uniformGAN-basedgeneratorplug-and-play
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper tries to show that the main obstacle in deep-prior blind motion deblurring is not the image prior but the way the blur kernel is initialized and searched. It proposes a two-part pretrained module: a GAN-based generator that encodes motion-blur kernels in a compact latent space, and an encoder that predicts a good latent code directly from the blurry image. Plugging this module into existing DIP-based and diffusion-based deblurring methods is said to improve their accuracy and stability, and to make non-uniform deblurring work without extra handcrafted kernel priors. A sympathetic reader would care because the module is presented as a reusable kernel prior rather than a full new deblurring pipeline, so its claimed benefit would compound across many existing methods.

What carries the argument

The load-bearing object is the latent kernel manifold: the image of a pre-trained GAN-based kernel generator. Because the generator compresses kernels into a low-dimensional latent space, optimizing the latent code (or, better, the first-layer feature map of the generator) keeps the search on plausible kernel shapes. The kernel initializer, an encoder mapping the blurry image to a latent code, gives the optimizer a warm start in that manifold. Together they turn kernel estimation from an unconstrained high-dimensional problem into a constrained one.

What would settle it

Run the pretrained kernel generator and initializer on a test set of camera-shake kernels that were recorded by inertial sensors and compare recovered kernels with the recorded trajectories; if initialization outside the synthetic manifold produces kernels that stay on the manifold but miss the true trajectory, and deblurring quality degrades accordingly, the transfer assumption fails. A simpler check is to ablate the encoder by restarting optimization from random latent codes drawn from the generator's prior and measure whether the reported gains disappear.

Watch

Extended reading notes

Core claim

The central claim is that the instability of deep-prior blind motion deblurring comes from searching for the blur kernel in the full, high-dimensional kernel space, where random starting points fall into bad local minima. To fix this, the paper pre-trains a GAN that maps a low-dimensional latent code to realistic motion-blur kernels and an encoder that maps the blurry image to a good latent code for that GAN. During deblurring the kernel is optimized only inside the generator's output space, initialized by the encoder, which confines the solution to a compact latent kernel manifold. The paper reports that this constraint improves four existing deblurring frameworks built on deep image priors or diffusion models, and that the same pretrained kernel prior transfers to non-uniform blur where many spatially varying kernels must be estimated together.

Load-bearing premise

The pretrained kernel manifold, learned from synthetic uniform blur kernels, is assumed to contain the real, spatially varying test-time kernels, so no retraining or adaptation is needed when the module is transferred.

Editorial extensions

If this is right

  • Existing deep-prior deblurring methods can be upgraded by swapping in the pretrained kernel prior, gaining several dB of PSNR and better perceptual scores on synthetic and real blurred images.
  • Random-restart sensitivity that produced widely divergent kernels in DIP-based methods is largely removed, making outputs more reproducible across runs.
  • The same pretrained components extend to non-uniform blur, where many spatially varying kernels are estimated jointly, without adding handcrafted kernel regularizers.
  • Large kernels near 75 by 75 pixels become tractable because optimization is confined to a compact manifold instead of the full kernel space.
  • Diffusion-based deblurring also improves when the kernel comes from the latent prior rather than from a separately trained network or handcrafted regularization.

Reading between the lines

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

  • If the latent-kernel-prior recipe is as general as it looks, it should transfer to other blind inverse problems with structured unknown operators, such as unknown super-resolution kernels or atmospheric point-spread functions, as long as a generator can be trained on the operator family; the paper demonstrates only motion blur.
  • The first-layer feature-map optimization suggests a tunable trade-off between manifold restriction and search flexibility; one could test how results degrade as the optimized layer moves deeper or the latent dimension shrinks, which would map where the sweet spot lies.
  • Because the generator is frozen during optimization, any test-time kernel outside the learned manifold cannot be recovered; an extension would be to detect low-confidence encoder initializations and adaptively widen the search or fine-tune the generator.
Share X Bluesky LinkedIn Reddit HN

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 GLKM, a plug-and-play generative latent kernel prior for blind motion deblurring (BMD). The method pre-trains a GAN-based kernel generator on synthetic motion blur kernels and a kernel initializer (a ResNet encoder) that maps a blurry image to the latent code of the generator. During deblurring, the kernel is optimized in the first-layer feature space of the generator rather than in the low-dimensional latent code, and is initialized by the encoder's prediction. GLKM is integrated into four existing BMD frameworks—DIP, VDIP, BIRD, and BlindDPS—and is extended to non-uniform BMD via the SVOLA formulation with a global kernel as a coherent initialization. Experiments on synthetic datasets, the Lai benchmark, AFHQ-dog, CelebAHQ, and ImageNet report consistent improvements over the corresponding baselines and state-of-the-art results on Lai's non-uniform dataset.

Significance. If the reported gains hold, the plug-and-play nature of GLKM is a practical strength: it improves several deep-prior BMD baselines without retraining their image generators, and it extends to non-uniform blur without adding hand-crafted kernel priors. The integration with multiple image priors (DIP, VDIP, diffusion-based BIRD and BlindDPS) and the public code release are commendable. However, the central conceptual claim—that constraining the kernel to a compact latent manifold alleviates the sensitivity to kernel initialization—is not directly evidenced by the experiments as presented. The paper's main contribution is therefore promising but under-validated; the missing ablations and variance analysis are needed to support the headline assertion.

major comments (5)
  1. [Sec. V-A, Eqs. (5)-(6), Tables I-II] The experiments never isolate the proposed generative manifold constraint from the learned kernel initializer. In every comparison, the baseline uses its default (usually random) kernel initialization and free-form kernel optimization, while GLKM uses both the encoder's predicted initialization and optimization in the generator's first-layer feature space w_k. To support the claim that the compact latent manifold itself stabilizes BMD, the paper should include at least two ablations: (i) baseline DIP/VDIP with the same E(y) initialization but optimizing the kernel directly in the original kernel space, and (ii) GLKM with a random latent initialization instead of E(y). Without these, the reported gains could be attributed entirely to the supervised initializer, and the central assertion is unsupported. In addition, the paper asserts in Sec. V-A that optimizing w_k is better than optimizing z_k, but no experiment compares these two optimization spaces.
  2. [Sec. III, Fig. 1] The paper's motivation is that BMD is extremely sensitive to the initial blur kernel, yet no quantitative evidence of reduced sensitivity is provided. Fig. 1 shows two anecdotal runs of SelfDeblur with different random initializations, but the paper reports no variance or repeated-run statistics for any baseline or for GLKM. Since the proposed method is specifically designed to stabilize the solution, the authors should report mean and standard deviation (or at least per-run results) over multiple random restarts for both the baselines and GLKM on a subset of the test images. A single run per method is insufficient to substantiate a sensitivity-reduction claim.
  3. [Sec. VII, Pre-training Setup; Sec. IV-B] The kernel generator and initializer are pre-trained only on synthetic uniform blur kernels (following [20] or [30]) and then applied directly to the non-uniform Lai dataset, whose kernels are constructed from inertial sensor recordings of real camera motion. The paper asserts transferability without presenting evidence that the learned generator's support includes such kernels. If the synthetic manifold does not contain the true test-time kernels, the manifold constraint would bias the solution toward incorrect kernels. The authors should provide a kernel-space transfer analysis, for example by projecting the ground-truth Lai kernels through the pre-trained generator and measuring reconstruction error, or by comparing estimated kernels with ground truth on the non-uniform dataset.
  4. [Sec. VIII; Sec. V-A.1] The manuscript repeatedly defers essential content to a supplementary file that is not present in the arXiv submission: the detailed VDIP-GLKM implementation and BlindDPS integration are described only in the supplementary material, and the conclusion states that limitations are discussed in the supplementary material. As a result, key parts of the method and the authors' own caveats are unavailable for assessment. The supplementary should be made available, or the relevant details and limitations should be summarized in the main text.
  5. [Tables V and VI] There is a conspicuous anomaly in the non-uniform results: VDIP-Extreme-GLKM achieves SSIM 0.370 on the synthetic non-uniform dataset (Table V), far below DIP-GLKM (0.807) and even below VDIP-Sparse-GLKM (0.584). In Table VI, VDIP-Extreme-GLKM is consistently worse than VDIP-Std-GLKM across all categories, which contradicts the uniform experiment where VDIP-Extreme-GLKM is competitive with or better than other variants. This pattern suggests a possible bug or reporting error in the non-uniform VDIP-Extreme implementation; please verify and correct, or explain why this variant degrades in the non-uniform setting.
minor comments (5)
  1. [Eq. (4)] The hyperparameter λ is fixed to 0.1, but the paper provides no sensitivity analysis for this choice. A brief experiment varying λ would help establish robustness.
  2. [Algorithm 1] The notation in Algorithm 1 is confusing: line 10 sets θ0 = θt−1_E, but the outer loop uses θt−1_E as the current parameter. Please clarify the iteration indices and the role of the inner L-loop.
  3. [Fig. 3 caption] The caption contains a typo: 'Esitimated Kernels' should be 'Estimated Kernels'.
  4. [Sec. VII, Comparison Methods] The comparison list for non-uniform deblurring includes methods up to 2023; since the paper claims state-of-the-art performance, it would be useful to compare against more recent non-uniform deblurring methods from 2024–2025 if any are available.
  5. [Sec. VI-A, Implementation Details] The paper states that baselines were run with 'officially default settings' or tuned for best performance, but it does not specify which setting was used for each comparison method. This information is needed for reproducibility.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation: the latent kernel prior and initializer are learned from synthetic kernels and tested against external baselines; the central claims are empirical, not self-referential.

full rationale

We found no step in which a predicted quantity is identical to a fitted input by construction, or in which a load-bearing premise is justified solely by a self-citation. The kernel generator Gk is pre-trained on synthetic blur kernels (Sec. IV-B) and the initializer E is trained through GAN inversion to map blurry images to latent codes (Eq. 4, Alg. 1); the final deblurring output is obtained by optimizing a reconstruction loss over wk and theta_x (Alg. 2-4) and is compared against external baselines. The initializer being trained through the same generator is a modeling coupling, not circular reasoning: E's quality is bounded by Gk's manifold, but the evaluation still requires the optimization to fit the observed blurry image. The paper is a disclosed extension of the authors' ECCV 2024 paper [37]; that citation is descriptive and non-load-bearing because the method is fully specified and tested in the present text. We note two non-circular weaknesses: the experiments do not isolate the effect of the manifold constraint from the learned initialization (no DIP + E-init with free kernel optimization, and no random-latent-init ablation), so the 'sensitivity alleviation' claim is not directly tested; and the conclusion defers to limitations in a supplementary file that is absent from the arXiv text. These are evidence gaps, not circularity.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

No new physical entities are posited; the kernel generator and initializer are trained neural networks, not theoretical constructs. The free parameters are standard hyperparameters chosen by hand, and the axioms are the domain and architectural assumptions the empirical claims depend on.

free parameters (3)
  • lambda in Eq. (4) (initializer loss weight) = 0.1
    Hand-chosen scalar balancing the kernel-space L1 term and latent-space L2 term when training the encoder; affects the quality of the learned initializer.
  • Optimization steps T for DIP-based BMD = 5,000
    Hand-chosen iteration count used in Algorithms 2-4; results may depend on this value.
  • SVOLA grid size P = 5 by 10
    Hand-chosen number of local kernels in the non-uniform model; directly controls the degrees of freedom of the estimated kernel field.
assumptions (5)
  • domain assumption Motion blur kernels lie on a low-dimensional manifold that a DCGAN can learn from synthesized trajectories (Sec. IV-B).
    The whole method relies on the kernel generator faithfully representing the true kernel distribution; if the manifold is inaccurate, the optimization is wrongly constrained.
  • domain assumption The kernel initializer trained on synthetic blurry images transfers to real-world and non-uniform blur (Sec. VII, 'Pre-training Setup').
    Non-uniform experiments use the uniform-trained E and G_k without retraining; distribution shift would break the claimed transferability.
  • ad hoc to paper Optimizing in the generator's first-layer feature map w_k yields better solutions than optimizing the low-dimensional latent code z_k (Sec. V-A).
    Stated as inspired by StyleGAN, but no derivation or ablation in the main text supports this claim.
  • ad hoc to paper The alternating optimization in Algorithm 1 converges to a useful initializer (Sec. IV-C).
    The bilevel problem (4) is solved by heuristic alternation without convergence guarantees.
  • domain assumption All local kernels in non-uniform blur share the camera-motion manifold and can be initialized with a single global kernel (Sec. V-B).
    The non-uniform framework initializes every local kernel with the same global prediction, assuming the kernels are related through camera motion.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Generative Latent Kernel Modeling for Blind Motion Deblurring." pith.science (2026). https://pith.science/paper/DHGK7THA

@misc{pith2026250709285,
  author       = {Pith},
  title        = {Pith review of: Generative Latent Kernel Modeling for Blind Motion Deblurring},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DHGK7THA}},
  note         = {Machine review of arXiv:2507.09285}
}
read the original abstract

Deep prior-based approaches have demonstrated remarkable success in blind motion deblurring (BMD) recently. These methods, however, are often limited by the high non-convexity of the underlying optimization process in BMD, which leads to extreme sensitivity to the initial blur kernel. To address this issue, we propose a novel framework for BMD that leverages a deep generative model to encode the kernel prior and induce a better initialization for the blur kernel. Specifically, we pre-train a kernel generator based on a generative adversarial network (GAN) to aptly characterize the kernel's prior distribution, as well as a kernel initializer to provide a well-informed and high-quality starting point for kernel estimation. By combining these two components, we constrain the BMD solution within a compact latent kernel manifold, thus alleviating the aforementioned sensitivity for kernel initialization. Notably, the kernel generator and initializer are designed to be easily integrated with existing BMD methods in a plug-and-play manner, enhancing their overall performance. Furthermore, we extend our approach to tackle blind non-uniform motion deblurring without the need for additional priors, achieving state-of-the-art performance on challenging benchmark datasets. The source code is available at https://github.com/dch0319/GLKM-Deblur.

Figures

Figures reproduced from arXiv: 2507.09285 by the authors.

Figure 1
Figure 1. Illustration of the initialization effect of the DIP-based BMD. The [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Overview of the Proposed BMD Framework. The framework incorporates a pre-trained kernel generator and initializer, where the image generator [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Illustration of our method’s effectiveness. The first row shows results [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Visual comparisons on our synthetic dataset. The estimated blur kernels are pasted at the top-left corners if available. More comparisons can be found [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Visual comparisons on Lai et al.’s synthetic dataset. The estimated blur kernels are pasted at the top-left corners if available. More comparisons can be found in the supplementary material. TABLE III QUANTITATIVE COMPARISONS OF VARIOUS METHODS ON LAI ET AL.’S REAL DAT…
Figure 6
Figure 6. Figure 6: Visual results on Lai et al.’s real dataset. The estimated blur kernels are pasted at the top-left corners if available. More comparisons can be found in the supplementary material. TABLE IV QUANTITATIVE RESULTS OF VARIOUS METHODS ON AFHQ-DOG [96], CELEBAHQ [97], AND I…
Figure 7
Figure 7. Figure 7: Visual comparison of different BMD methods on AFHQ-Dog [ [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Visual comparison of different BMD methods on CelebAHQ [ [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]
Figure 9
Figure 9. Figure 9: Visual comparison of different BMD methods on Imagenet [ [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]
Figure 10
Figure 10. Figure 10: Visual results on our non-uniform synthetic dataset. More comparisons can be found in the supplementary material. [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]
Figure 11
Figure 11. Figure 11: Visual results on Lai et al.’s non-uniform blurred dataset. More comparisons can be found in the supplementary material. originally designed for uniform BMD, we adapt them for non￾uniform cases by integrating the SVOLA formulation (Eq. 11) into their respective framew…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

103 extracted references · 75 canonical work pages

  1. [20]

    Deblurgan: Blind motion deblurring using conditional adversarial networks,

    O. Kupyn, V . Budzan, M. Mykhailych, D. Mishkin, and J. Matas, “Deblurgan: Blind motion deblurring using conditional adversarial networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 8183–8192

    2018

  2. [30]

    Parallel diffusion models of operator and image for blind inverse problems,

    H. Chung, J. Kim, S. Kim, and J. C. Ye, “Parallel diffusion models of operator and image for blind inverse problems,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 6059–6069. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 14

    work page 2023

  3. [1]

    Fast image deconvolution using hyper- laplacian priors,

    D. Krishnan and R. Fergus, “Fast image deconvolution using hyper- laplacian priors,” in Advances in Neural Information Processing Sys- tems, Y . Bengio, D. Schuurmans, J. Lafferty, C. Williams, and A. Cu- lotta, Eds., vol. 22. Curran Associates, Inc., 2009

    2009

  4. [2]

    Blind deconvolution using a normalized sparsity measure,

    D. Krishnan, T. Tay, and R. Fergus, “Blind deconvolution using a normalized sparsity measure,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2011, pp. 233–240

    2011

  5. [3]

    High-quality motion deblurring from a single image,

    Q. Shan, J. Jia, and A. Agarwala, “High-quality motion deblurring from a single image,” ACM Transactions on Graphics (TOG), vol. 27, no. 3, p. 1–10, 2008

    2008

  6. [4]

    Unnatural l0 sparse representation for natural image deblurring,

    L. Xu, S. Zheng, and J. Jia, “Unnatural l0 sparse representation for natural image deblurring,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 1107– 1114

    2013

  7. [5]

    Deblurring text images via l0- regularized intensity and gradient prior,

    J. Pan, Z. Hu, Z. Su, and M.-H. Yang, “Deblurring text images via l0- regularized intensity and gradient prior,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2014, pp. 2901–2908

    2014

  8. [6]

    l0-regularized intensity and gradient prior for deblurring text images and beyond,

    ——, “ l0-regularized intensity and gradient prior for deblurring text images and beyond,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 2, pp. 342–355, 2016

    2016

Show all 103 references
  1. [7]

    Blind deblurring using internal patch recurrence,

    T. Michaeli and M. Irani, “Blind deblurring using internal patch recurrence,” in Proceedings of the European Conference on Computer Vision (ECCV). Springer, 2014, pp. 783–798

    2014

  2. [8]

    Deblurring images via dark channel prior,

    J. Pan, D. Sun, H. Pfister, and M.-H. Yang, “Deblurring images via dark channel prior,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 10, pp. 2315–2328, 2017

    2017

  3. [9]

    Removing camera shake from a single photograph,

    R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman, “Removing camera shake from a single photograph,” in Proceedings of ACM SIGGRAPH Conference , 2006, pp. 787–794

    2006

  4. [10]

    Variational dirichlet blur kernel estimation,

    X. Zhou, J. Mateos, F. Zhou, R. Molina, and A. K. Katsaggelos, “Variational dirichlet blur kernel estimation,” IEEE Transactions on Image Processing (TIP) , vol. 24, no. 12, pp. 5127–5139, 2015

    2015

  5. [11]

    Blind image deconvolution: problem formulation and existing approaches,

    T. E. Bishop, S. D. Babacan, B. Amizic, A. K. Katsaggelos, T. Chan, and R. Molina, “Blind image deconvolution: problem formulation and existing approaches,” in Blind Image Deconvolution. CRC press, 2017, pp. 21–62

    2017

  6. [12]

    Deep variational network toward blind image restoration,

    Z. Yue, H. Yong, Q. Zhao, L. Zhang, D. Meng, and K.-Y . K. Wong, “Deep variational network toward blind image restoration,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 46, no. 11, pp. 7011–7026, 2024

    2024

  7. [13]

    Fast motion deblurring,

    S. Cho and S. Lee, “Fast motion deblurring,” in ACM SIGGRAPH Asia 2009 papers, 2009, pp. 1–8

    2009

  8. [14]

    Total variation blind deconvolution,

    T. F. Chan and C.-K. Wong, “Total variation blind deconvolution,” IEEE Transactions on Image Processing (TIP) , vol. 7, no. 3, pp. 370– 375, 1998

    1998

  9. [15]

    Image deblurring via extreme channels prior,

    Y . Yan, W. Ren, Y . Guo, R. Wang, and X. Cao, “Image deblurring via extreme channels prior,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 4003– 4011

    2017

  10. [16]

    A bayesian approach to blind deconvolution based on dirichlet distributions,

    R. Molina, A. K. Katsaggelos, J. Abad, and J. Mateos, “A bayesian approach to blind deconvolution based on dirichlet distributions,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 4. IEEE, 1997, pp. 2809–2812

    1997

  11. [17]

    Understanding and evaluating blind deconvolution algorithms,

    A. Levin, Y . Weiss, F. Durand, and W. T. Freeman, “Understanding and evaluating blind deconvolution algorithms,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2009, pp. 1964–1971

    2009

  12. [18]

    Deep multi-scale convolutional neural network for dynamic scene deblurring,

    S. Nah, T. Hyun Kim, and K. Mu Lee, “Deep multi-scale convolutional neural network for dynamic scene deblurring,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 3883–3891

    2017

  13. [19]

    Scale-recurrent network for deep image deblurring,

    X. Tao, H. Gao, X. Shen, J. Wang, and J. Jia, “Scale-recurrent network for deep image deblurring,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 8174–8182

    2018

  14. [21]

    Deblurgan-v2: De- blurring (orders-of-magnitude) faster and better,

    O. Kupyn, T. Martyniuk, J. Wu, and Z. Wang, “Deblurgan-v2: De- blurring (orders-of-magnitude) faster and better,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2019, pp. 8878–8887

    2019

  15. [22]

    Multi-stage progressive image restoration,

    S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, M.-H. Yang, and L. Shao, “Multi-stage progressive image restoration,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 14 821–14 831

    2021

  16. [23]

    Restormer: Efficient transformer for high-resolution image restoration,

    S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, and M.- H. Yang, “Restormer: Efficient transformer for high-resolution image restoration,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022, pp. 5728–5739

    2022

  17. [24]

    Deep image prior,

    D. Ulyanov, A. Vedaldi, and V . Lempitsky, “Deep image prior,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 9446–9454

    2018

  18. [25]

    Neural blind deconvolution using deep priors,

    D. Ren, K. Zhang, Q. Wang, Q. Hu, and W. Zuo, “Neural blind deconvolution using deep priors,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 3341–3350

    2020

  19. [26]

    Blind image deconvolution using variational deep image prior,

    D. Huo, A. Masoumzadeh, R. Kushol, and Y .-H. Yang, “Blind image deconvolution using variational deep image prior,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 10, pp. 11 472–11 483, 2023

    2023

  20. [27]

    Self-supervised blind motion deblurring with deep expectation maximization,

    J. Li, W. Wang, Y . Nan, and H. Ji, “Self-supervised blind motion deblurring with deep expectation maximization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 13 986–13 996

    2023

  21. [28]

    Denoising diffusion probabilistic models,

    J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in Neural Information Processing Systems , vol. 33, pp. 6840–6851, 2020

    2020

  22. [29]

    Score-based generative modeling through stochastic differential equations,

    Y . Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, “Score-based generative modeling through stochastic differential equations,” in International Conference on Learning Representations, 2021. [Online]. Available: https://openreview.net/ forum?id=PxTIG12RRHS

    2021

  23. [31]

    Blind image restoration via fast diffusion inversion,

    H. Chihaoui, A. Lemkhenter, and P. Favaro, “Blind image restoration via fast diffusion inversion,” in Advances in Neural Information Pro- cessing Systems, vol. 37. Curran Associates, Inc., 2024, pp. 34 513– 34 532

    2024

  24. [32]

    Exploiting deep generative prior for versatile image restoration and manipulation,

    X. Pan, X. Zhan, B. Dai, D. Lin, C. C. Loy, and P. Luo, “Exploiting deep generative prior for versatile image restoration and manipulation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 11, pp. 7474–7489, 2021

    2021

  25. [33]

    Glean: Generative latent bank for large-factor image super-resolution,

    K. C. Chan, X. Wang, X. Xu, J. Gu, and C. C. Loy, “Glean: Generative latent bank for large-factor image super-resolution,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 14 245–14 254

    2021

  26. [34]

    Gan prior embedded network for blind face restoration in the wild,

    T. Yang, P. Ren, X. Xie, and L. Zhang, “Gan prior embedded network for blind face restoration in the wild,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 672–681

    2021

  27. [35]

    Towards real-world blind face restoration with generative facial prior,

    X. Wang, Y . Li, H. Zhang, and Y . Shan, “Towards real-world blind face restoration with generative facial prior,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 9168–9178

    2021

  28. [36]

    Generative adversarial nets,

    I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y . Bengio, “Generative adversarial nets,” Advances in Neural Information Processing Systems , vol. 27, 2014

    2014

  29. [37]

    Blind image deconvolution by generative-based kernel prior and initializer via latent encoding,

    J. Zhang, Z. Yue, H. Wang, Q. Zhao, and D. Meng, “Blind image deconvolution by generative-based kernel prior and initializer via latent encoding,” in Proceedings of the European Conference on Computer Vision (ECCV). Springer, 2024, pp. 73–92

    2024

  30. [38]

    A comparative study for single image blind deblurring,

    W.-S. Lai, J.-B. Huang, Z. Hu, N. Ahuja, and M.-H. Yang, “A comparative study for single image blind deblurring,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 1701–1709

    2016

  31. [39]

    Blind motion deblurring from a single image using sparse approximation,

    J.-F. Cai, H. Ji, C. Liu, and Z. Shen, “Blind motion deblurring from a single image using sparse approximation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2009, pp. 104–111

    2009

  32. [40]

    Edge-based blur kernel estimation using patch priors,

    L. Sun, S. Cho, J. Wang, and J. Hays, “Edge-based blur kernel estimation using patch priors,” in IEEE international conference on computational photography (ICCP) . IEEE, 2013, pp. 1–8

    2013

  33. [41]

    Image deblurring via enhanced low-rank prior,

    W. Ren, X. Cao, J. Pan, X. Guo, W. Zuo, and M.-H. Yang, “Image deblurring via enhanced low-rank prior,” IEEE Transactions on Image Processing (TIP), vol. 25, no. 7, pp. 3426–3437, 2016

    2016

  34. [42]

    Blind image deblurring using spectral properties of convolution operators,

    G. Liu, S. Chang, and Y . Ma, “Blind image deblurring using spectral properties of convolution operators,” IEEE Transactions on Image Processing (TIP), vol. 23, no. 12, pp. 5047–5056, 2014

    2014

  35. [43]

    Total variation blind deconvolution: The devil is in the details,

    D. Perrone and P. Favaro, “Total variation blind deconvolution: The devil is in the details,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 2909– 2916

    2014

  36. [44]

    Learning iteration- wise generalized shrinkage–thresholding operators for blind deconvo- lution,

    W. Zuo, D. Ren, D. Zhang, S. Gu, and L. Zhang, “Learning iteration- wise generalized shrinkage–thresholding operators for blind deconvo- lution,” IEEE Transactions on Image Processing (TIP) , vol. 25, no. 4, pp. 1751–1764, 2016

    2016

  37. [45]

    Learning a convolutional neural network for non-uniform motion blur removal,

    J. Sun, W. Cao, Z. Xu, and J. Ponce, “Learning a convolutional neural network for non-uniform motion blur removal,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 769–777

    2015

  38. [46]

    A neural approach to blind motion deblurring,

    A. Chakrabarti, “A neural approach to blind motion deblurring,” in Proceedings of the European Conference on Computer Vision (ECCV) . Springer, 2016, pp. 221–235

    2016

  39. [47]

    From motion blur to motion flow: A deep learning solution for removing heterogeneous motion blur,

    D. Gong, J. Yang, L. Liu, Y . Zhang, I. Reid, C. Shen, A. Van Den Hengel, and Q. Shi, “From motion blur to motion flow: A deep learning solution for removing heterogeneous motion blur,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)...

    2017

  40. [48]

    Physics-based generative adversarial models for image restoration and beyond,

    J. Pan, J. Dong, Y . Liu, J. Zhang, J. Ren, J. Tang, Y .-W. Tai, and M.-H. Yang, “Physics-based generative adversarial models for image restoration and beyond,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 7, pp. 2449–2462, 2020

    2020

  41. [49]

    Dark and bright channel prior embedded network for dynamic scene deblurring,

    J. Cai, W. Zuo, and L. Zhang, “Dark and bright channel prior embedded network for dynamic scene deblurring,” IEEE Transactions on Image Processing (TIP), vol. 29, pp. 6885–6897, 2020

    2020

  42. [50]

    Rethinking coarse-to-fine approach in single image deblurring,

    S.-J. Cho, S.-W. Ji, J.-P. Hong, S.-W. Jung, and S.-J. Ko, “Rethinking coarse-to-fine approach in single image deblurring,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 4641–4650

    2021

  43. [51]

    Simple baselines for image restoration,

    L. Chen, X. Chu, X. Zhang, and J. Sun, “Simple baselines for image restoration,” in Proceedings of the European Conference on Computer Vision (ECCV). Springer, 2022, pp. 17–33

    2022

  44. [52]

    Efficient frequency domain-based transformers for high-quality image deblurring,

    L. Kong, J. Dong, J. Ge, M. Li, and J. Pan, “Efficient frequency domain-based transformers for high-quality image deblurring,” in Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 5886–5895

    2023

  45. [53]

    Cascaded deep video deblurring using temporal sharpness prior and non-local spatial- temporal similarity,

    J. Pan, B. Xu, H. Bai, J. Tang, and M.-H. Yang, “Cascaded deep video deblurring using temporal sharpness prior and non-local spatial- temporal similarity,” IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, vol. 45, no. 8, pp. 9411–9425, 2023

    2023

  46. [54]

    Residual-conditioned opti- mal transport: towards structure-preserving unpaired and paired im- age restoration,

    X. Tang, X. Hu, X. Gu, and J. Sun, “Residual-conditioned opti- mal transport: towards structure-preserving unpaired and paired im- age restoration,” in International Conference on Machine Learning . PMLR, 2024, pp. 47 757–47 777

    2024

  47. [55]

    Learning efficient deep discriminative spatial and temporal networks for video deblurring,

    J. Pan, L. Sun, B. Xu, J. Dong, and J. Tang, “Learning efficient deep discriminative spatial and temporal networks for video deblurring,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025

    2025

  48. [56]

    Degradation-aware residual- conditioned optimal transport for unified image restoration,

    X. Tang, X. Gu, X. He, X. Hu, and J. Sun, “Degradation-aware residual- conditioned optimal transport for unified image restoration,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2025

    2025

  49. [57]

    Real-world blur dataset for learning and benchmarking deblurring algorithms,

    J. Rim, H. Lee, J. Won, and S. Cho, “Real-world blur dataset for learning and benchmarking deblurring algorithms,” in Proceedings of the European Conference on Computer Vision (ECCV) . Springer, 2020, pp. 184–201

    2020

  50. [58]

    Uncertainty- aware unsupervised image deblurring with deep residual prior,

    X. Tang, X. Zhao, J. Liu, J. Wang, Y . Miao, and T. Zeng, “Uncertainty- aware unsupervised image deblurring with deep residual prior,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 9883–9892

    2023

  51. [59]

    Blind image deconvolution using deep generative priors,

    M. Asim, F. Shamshad, and A. Ahmed, “Blind image deconvolution using deep generative priors,” IEEE Transactions on Computational Imaging, vol. 6, pp. 1493–1506, 2020

    2020

  52. [60]

    Flow- based kernel prior with application to blind super-resolution,

    J. Liang, K. Zhang, S. Gu, L. Van Gool, and R. Timofte, “Flow- based kernel prior with application to blind super-resolution,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 10 601–10 610

    2021

  53. [61]

    Blind image super-resolution with elaborate degradation modeling on noise and kernel,

    Z. Yue, Q. Zhao, J. Xie, L. Zhang, D. Meng, and K.-Y . K. Wong, “Blind image super-resolution with elaborate degradation modeling on noise and kernel,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022, pp. 2128–2138

    2022

  54. [62]

    ” double-dip

    Y . Gandelsman, A. Shocher, and M. Irani, “” double-dip”: unsupervised image decomposition via coupled deep-image-priors,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog- nition (CVPR), 2019, pp. 11 026–11 035

    2019

  55. [63]

    Retinexdip: A unified deep framework for low-light image enhancement,

    Z. Zhao, B. Xiong, L. Wang, Q. Ou, L. Yu, and F. Kuang, “Retinexdip: A unified deep framework for low-light image enhancement,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 32, no. 3, pp. 1076–1088, 2021

    2021

  56. [64]

    Pet image reconstruction using deep image prior,

    K. Gong, C. Catana, J. Qi, and Q. Li, “Pet image reconstruction using deep image prior,” IEEE Transactions on Medical Imaging , vol. 38, no. 7, pp. 1655–1665, 2018

    2018

  57. [65]

    Hyperspectral mixed noise removal via spatial-spectral constrained unsupervised deep image prior,

    Y .-S. Luo, X.-L. Zhao, T.-X. Jiang, Y .-B. Zheng, and Y . Chang, “Hyperspectral mixed noise removal via spatial-spectral constrained unsupervised deep image prior,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 14, pp. 9435– 9449, 2021

    2021

  58. [66]

    Pulse: Self- supervised photo upsampling via latent space exploration of generative models,

    S. Menon, A. Damian, S. Hu, N. Ravi, and C. Rudin, “Pulse: Self- supervised photo upsampling via latent space exploration of generative models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 2437–2445

    2020

  59. [67]

    Diffusion image prior,

    H. Chihaoui and P. Favaro, “Diffusion image prior,” 2025. [Online]. Available: https://arxiv.org/abs/2503.21410

    2025 arXiv

  60. [68]

    Gibbsddrm: A partially collapsed gibbs sampler for solving blind inverse problems with denoising diffusion restoration,

    N. Murata, K. Saito, C.-H. Lai, Y . Takida, T. Uesaka, Y . Mitsufuji, and S. Ermon, “Gibbsddrm: A partially collapsed gibbs sampler for solving blind inverse problems with denoising diffusion restoration,” in International Conference on Machine Learning . PMLR, 2023, pp. 25 501–25 522

    2023

  61. [69]

    Gan inversion: A survey,

    W. Xia, Y . Zhang, Y . Yang, J.-H. Xue, B. Zhou, and M.-H. Yang, “Gan inversion: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 3, pp. 3121–3138, 2022

    2022

  62. [70]

    Navigating image restoration with var’s distribution alignment prior,

    S. Wang, N. Zheng, J. Huang, and F. Zhao, “Navigating image restoration with var’s distribution alignment prior,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 7559–7569

    2025

  63. [71]

    JoIN: Joint GANs inversion for intrinsic image decomposition,

    V . Shah, S. Lazebnik, and J. Philip, “JoIN: Joint GANs inversion for intrinsic image decomposition,” Transactions on Machine Learning JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 15 Research, 2025. [Online]. Available: https://openreview.net/forum?id= JEHIVfjmOf

    2021

  64. [72]

    Auto-encoding variational bayes,

    D. P. Kingma, M. Welling et al. , “Auto-encoding variational bayes,” 2013

    2013

  65. [73]

    Nice: Non-linear independent components estimation,

    L. Dinh, D. Krueger, and Y . Bengio, “Nice: Non-linear independent components estimation,” arXiv preprint arXiv:1410.8516 , 2014

    2014 arXiv

  66. [74]

    Density estimation using real NVP,

    L. Dinh, J. Sohl-Dickstein, and S. Bengio, “Density estimation using real NVP,” in International Conference on Learning Representations ,

  67. [75]

    Glow: Generative flow with invertible 1x1 convolutions,

    D. P. Kingma and P. Dhariwal, “Glow: Generative flow with invertible 1x1 convolutions,” Advances in Neural Information Processing Sys- tems, vol. 31, 2018

    2018

  68. [76]

    Unsupervised representation learning with deep convolutional generative adversarial networks,

    A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv preprint arXiv:1511.06434 , 2015

    2015 arXiv

  69. [77]

    Ex- plicitly minimizing the blur error of variational autoencoders,

    G. Bredell, K. Flouris, K. Chaitanya, E. Erdil, and E. Konukoglu, “Ex- plicitly minimizing the blur error of variational autoencoders,” in The Eleventh International Conference on Learning Representations , 2023. [Online]. Available: https://openreview.net/forum?id=9krnQ-ue9M

    2023

  70. [78]

    Col- laborative learning for faster stylegan embedding,

    S. Guan, Y . Tai, B. Ni, F. Zhu, F. Huang, and X. Yang, “Col- laborative learning for faster stylegan embedding,” arXiv preprint arXiv:2007.01758, 2020

    2007 arXiv

  71. [79]

    A style-based generator architecture for generative adversarial networks,

    T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 4401–4410

    2019

  72. [80]

    Denoising diffusion implicit mod- els,

    J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit mod- els,” in International Conference on Learning Representations , 2021. [Online]. Available: https://openreview.net/forum?id=St1giarCHLP

    2021

  73. [81]

    Space-variant single- image blind deconvolution for removing camera shake,

    S. Harmeling, H. Michael, and B. Sch ¨olkopf, “Space-variant single- image blind deconvolution for removing camera shake,” Advances in Neural Information Processing Systems , vol. 23, 2010

    2010

  74. [82]

    Efficient filter flow for space-variant multiframe blind deconvolution,

    M. Hirsch, S. Sra, B. Sch ¨olkopf, and S. Harmeling, “Efficient filter flow for space-variant multiframe blind deconvolution,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2010, pp. 607–614

    2010

  75. [83]

    Image deblurring using inertial measurement sensors,

    N. Joshi, S. B. Kang, C. L. Zitnick, and R. Szeliski, “Image deblurring using inertial measurement sensors,” ACM Transactions on Graphics (TOG), vol. 29, no. 4, pp. 1–9, 2010

    2010

  76. [84]

    Blind motion deblur- ring with pixel-wise kernel estimation via kernel prediction networks,

    G. Carbajal, P. Vitoria, J. Lezama, and P. Mus ´e, “Blind motion deblur- ring with pixel-wise kernel estimation via kernel prediction networks,” IEEE Transactions on Computational Imaging , vol. 9, pp. 928–943, 2023

    2023

  77. [85]

    Blind image deblurring with outlier handling,

    J. Dong, J. Pan, Z. Su, and M.-H. Yang, “Blind image deblurring with outlier handling,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2017, pp. 2478–2486

    2017

  78. [86]

    Deblurring using analysis-synthesis net- works pair,

    A. Kaufman and R. Fattal, “Deblurring using analysis-synthesis net- works pair,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 5811–5820

    2020

  79. [87]

    Cross-scale self-supervised blind image deblurring via implicit neural representation,

    T. Zhang, Y . Quan, and H. Ji, “Cross-scale self-supervised blind image deblurring via implicit neural representation,” in The Thirty-eighth Annual Conference on Neural Information Processing Systems , 2024

    2024

  80. [88]

    Deep residual learning for image recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778

    2016

  81. [89]

    The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale,

    A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont- Tuset, S. Kamali, S. Popov, M. Malloci, A. Kolesnikov et al. , “The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale,” International Journal of...

    1956

  82. [90]

    Adam: A method for stochastic optimization,

    D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014

    2014 arXiv

  83. [91]

    The unreasonable effectiveness of deep features as a perceptual metric,

    R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 586–595

    2018

  84. [92]

    Making a “completely blind

    A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a “completely blind” image quality analyzer,”IEEE Signal Processing Letters, vol. 20, no. 3, pp. 209–212, 2012

    2012

  85. [93]

    Blind/referenceless image spatial quality evaluator,

    A. Mittal, A. K. Moorthy, and A. C. Bovik, “Blind/referenceless image spatial quality evaluator,” in 2011 conference record of the forty fifth asilomar conference on signals, systems and computers (ASILOMAR) . IEEE, 2011, pp. 723–727

    2011

  86. [94]

    Blind image quality evaluation using perception based features,

    N. Venkatanath, D. Praneeth, M. C. Bh, S. S. Channappayya, and S. S. Medasani, “Blind image quality evaluation using perception based features,” in 2015 twenty first national conference on communications (NCC). IEEE, 2015, pp. 1–6

    2015

  87. [95]

    Microsoft coco: Common objects in context,

    T.-Y . Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll ´ar, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in Proceedings of the European Conference on Computer Vision (ECCV). Springer, 2014, pp. 740–755

    2014

  88. [96]

    Stargan v2: Diverse image synthesis for multiple domains,

    Y . Choi, Y . Uh, J. Yoo, and J.-W. Ha, “Stargan v2: Diverse image synthesis for multiple domains,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 8188–8197

    2020

  89. [97]

    Large-scale celebfaces attributes (celeba) dataset,

    Z. Liu, P. Luo, X. Wang, and X. Tang, “Large-scale celebfaces attributes (celeba) dataset,” Retrieved August, vol. 15, no. 2018, p. 11, 2018

    2018

  90. [98]

    Imagenet: A large-scale hierarchical image database,

    J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Ieee, 2009, pp. 248–255

    2009

  91. [99]

    Non-uniform deblur- ring for shaken images,

    O. Whyte, J. Sivic, A. Zisserman, and J. Ponce, “Non-uniform deblur- ring for shaken images,” International Journal of Computer Vision , vol. 98, pp. 168–186, 2012

    2012

  92. [100]

    Event-guided multi-patch network with self-supervision for non-uniform motion deblurring,

    H. Zhang, L. Zhang, Y . Dai, H. Li, and P. Koniusz, “Event-guided multi-patch network with self-supervision for non-uniform motion deblurring,” International Journal of Computer Vision , vol. 131, no. 2, pp. 453–470, 2023

    2023

  93. [101]

    Self-supervised non-uniform kernel estimation with flow-based motion prior for blind image deblurring,

    Z. Fang, F. Wu, W. Dong, X. Li, J. Wu, and G. Shi, “Self-supervised non-uniform kernel estimation with flow-based motion prior for blind image deblurring,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 18 105– 18 114

    2023

  94. [102]

    From local to global: Edge profiles to camera motion in blurred images,

    S. Vasu and A. Rajagopalan, “From local to global: Edge profiles to camera motion in blurred images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 4447–4456

    2017

  95. [2017]

    Available: https://openreview.net/forum?id=HkpbnH9lx

    [Online]. Available: https://openreview.net/forum?id=HkpbnH9lx

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.