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REVIEW 2 major objections 5 minor 57 references

NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Results

T0 review · 2 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read The paper reports the first open challenge ranking of event-based image deblurring methods, with the winner at 42.79 dB PSNR on the HighREV test set.

desk verdict Useful first benchmark for event-based deblurring, but the 0.09 dB top-2 PSNR gap with inverted SSIM means the headline ranking is not statistically supported as stated. read the letter →

arxiv 2504.12401 v1 pith:KMWRNRY6 submitted 2025-04-16 cs.CV

Lei Sun , Andrea Alfarano , Peiqi Duan , Shaolin Su , Kaiwei Wang , Boxin Shi , Radu Timofte , Danda Pani Paudel
show 80 more authors
Luc Van Gool Qinglin Liu Wei Yu Xiaoqian Lv Lu Yang Shuigen Wang Shengping Zhang Xiangyang Ji Long Bao Yuqiang Yang Jinao Song Ziyi Wang Shuang Wen Heng Sun Kean Liu Mingchen Zhong Senyan Xu Zhijing Sun Jiaying Zhu Chengjie Ge Xingbo Wang Yidi Liu Xin Lu Xueyang Fu Zheng-Jun Zha Dawei Fan Dafeng Zhang Yong Yang Siru Zhang Qinghua Yang Hao Kang Huiyuan Fu Heng Zhang Hongyuan Yu Zhijuan Huang Shuoyan Wei Feng Li Runmin Cong Weiqi Luo Mingyun Lin Chenxu Jiang Hongyi Liu Lei Yu Weilun Li Jiajun Zhai Tingting Lin Shuang Ma Sai Zhou Zhanwen Liu Yang Wang Eiffel Chong Nuwan Bandara Thivya Kandappu Archan Misra Yihang Chen Zhan Li Weijun Yuan Wenzhuo Wang Boyang Yao Zhanglu Chen Yijing Sun Tianjiao Wan Zijian Gao Qisheng Xu Kele Xu Yukun Zhang Yu He Xiaoyan Xie Tao Fu Yashu Gautamkumar Patel Vihar Ramesh Jain Divesh Basina Rishik Ashili Manish Kumar Manjhi Sourav Kumar Prinon Benny Himanshu Ghunawat B Sri Sairam Gautam Anett Varghese Abhishek Yadav
This is my paper · ORCID
classification cs.CV
keywords event-basedvisionimagedeblurringeventcamerasbenchmarkchallengePSNRHighREVdatasetcross-modalfusiontransformerarchitectures
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 reports the first open challenge on event-based image deblurring, in which 199 people registered and 15 teams submitted working solutions. Its central claim is a ranking: on the 271 test images of the HighREV dataset, the first-place method reached a PSNR of 42.79 dB and an SSIM of 0.9196, with the next two submissions at 42.70 and 42.09 dB. The paper argues that the winning entries share a recipe — hybrid transformer-convolutional architectures, attention-based fusion of image and event features, pretrained initialization, and progressive training — and that these transferable design choices explain the results. If the ranking holds, it gives the field a reproducible reference point and a concrete set of architectural priors for event-guided deblurring.

What carries the argument

The load-bearing assessment machinery is the HighREV pairing of blurry images with asynchronous events and sharp ground truth, organized into fixed train/validation/test splits, plus the PSNR and SSIM metrics computed on 271 hidden test images. The mechanism that carries most solutions is cross-modal fusion: events are voxelized into a set of temporal bins and fed alongside the blurry image into an encoder-decoder whose attention blocks let image and event features guide each other, as in the EFNet and REFID baselines that many teams adapted. Supporting devices include triple-stream event voxelization at different temporal windows, sequence-level model ensembling, progressive enlargement of training patches, and pretrained initialization.

What would settle it

Run an independent audit of the released model weights: if any top finisher's predictions on validation or test images can only be explained by having seen the corresponding ground truth during training, the ranking collapses. More directly, retraining the top architecture from scratch on the official training split alone should reproduce its reported test PSNR to within a small tolerance.

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Extended reading notes

Core claim

The paper's discovery is the benchmark outcome itself: under a shared protocol using the HighREV dataset, which contributes 1,771 training sets, 421 validation sets, and 271 hidden test sets of blurry images, event streams, and sharp references, the submitted event-based deblurring methods separate cleanly in quality. The first-place entry, an encoder-decoder that voxelizes events at three temporal scales and uses shift-window attention plus channel attention, scores 42.79 PSNR and 0.9196 SSIM; second and third place score 42.70 / 0.9281 and 42.09 / 0.9300, respectively. A supporting observation is architectural: all top-three teams combine transformers with convolutional networks, several build on the EFNet cross-modal fusion baseline, and the winning team initializes its backbone with ImageNet-pretrained weights. The paper also reports that the third-place team reached the highest SSIM by switching between a video-oriented and a single-image model depending on test-sequence type.

Load-bearing premise

The ranking stands on the trust-based rule that participants never trained on the HighREV validation or test data, since the report describes no enforcement or post-hoc verification of that rule.

Editorial extensions

If this is right

  • The published code and pretrained weights for all 15 submissions make the top PSNR and SSIM numbers reproducible targets for future event-based deblurring methods.
  • The top-three architecture pattern suggests that transformer-convolution hybrids with cross-modal attention are a strong default design for fusing events and images.
  • Because the challenge imposed no limit on compute or model size, the scores represent an accuracy ceiling rather than a practical-efficiency trade-off.
  • The observation that a 512-by-512 patch size was optimal for one team indicates resolution-dependent training behavior that later methods must tune explicitly.
  • The scene-dependent ensemble that produced the best SSIM implies that no single model dominated all sequence types in the test set.

Reading between the lines

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

  • If the data-use rules were ever violated by a leading team, the whole ranking would be suspect; an independent audit comparing models trained with and without the validation split would settle this.
  • The assumption that event and image inputs are spatially aligned is baked into the challenge, so the rankings likely overstate performance on real event-camera deployments where calibration and synchronization are imperfect.
  • A natural extension is to measure the same fifteen methods on misaligned or noisy event streams to see which architectural choices are robust to the gap between benchmark and deployment conditions.
  • The wide PSNR spread from 42.79 down to 25.93 suggests that several submitted solutions are overfit to HighREV statistics; re-testing the released weights on a different event-deblurring dataset would reveal how much generalizes.
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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

2 major / 5 minor

Summary. The paper reports the organization and outcomes of the NTIRE 2025 challenge on event-based image deblurring. Using the HighREV dataset, 199 participants registered and 15 teams submitted valid results; the paper presents the leaderboard (Table 1), summarizes the main architectural ideas of the top teams, and describes each method in Section 4. The central factual claim is that IVISLAB ranks first in PSNR (42.79 dB), followed by MiVideoDeblur (42.70 dB) and 404NotFound (42.09 dB), with additional observations in Section 3.2 about the benefits of hybrid transformer/convolutional architectures, pretrained initialization, cross-modal fusion, progressive training, and Mamba-based designs.

Significance. If the reported ranking is accepted as an accurate empirical comparison, the paper is a useful snapshot of the current state of event-based image deblurring and provides the community with public code and pretrained weights for all submitted solutions. The method descriptions are informative and the challenge itself is a valuable benchmark. However, the strength of the central claim is limited by the absence of any statistical analysis around the leaderboard numbers: the first- and second-place PSNR gap is only 0.09 dB, the SSIM order is reversed, and no per-image variance, confidence interval, paired test, or per-sequence breakdown is provided. The fairness premise, which is load-bearing for the ranking, is also stated but not verified. These issues are addressable and do not undermine the utility of the dataset or the reported methods, but they do weaken the paper's main empirical conclusions as currently written.

major comments (2)
  1. [Section 3, Table 1] The central claim that IVISLAB is the winner is not statistically supported as reported. The PSNR difference between IVISLAB (42.79) and MiVideoDeblur (42.70) is 0.09 dB, while MiVideoDeblur has the higher SSIM (0.9281 vs. 0.9196). The paper reports no per-image variance, confidence interval, paired significance test, or per-sequence breakdown, even though Section 4.3.3 states that the HighREV test set consists of four sequences. With such a small gap and a contradictory secondary metric, the first-place ranking, and the Section 3.2 attributions of that result to triple-stream event voxels, ImageNet pretraining, and 512x512 patches, could be properties of this particular 271-image test sample rather than stable method differences. Please add per-sequence results and a paired test or bootstrap confidence interval, or qualify the ranking accordingly.
  2. [Section 3.3] The integrity of the leaderboard rests on participants not using the HighREV validation set or the HighREV test blurry images during training, but the manuscript only states that these uses were 'strictly prohibited' and 'not allowed.' No enforcement mechanism, monitoring procedure, or post-hoc verification (for example, inspection of submitted code, model behavior checks, or data provenance analysis) is described. As written, one load-bearing premise of the ranking is unverified. The authors should either document how compliance was ensured or explicitly state that compliance was based on self-reporting, so that readers can calibrate the strength of the conclusions.
minor comments (5)
  1. [Section 4.1.3] The implementation details for TEIDNet specify Ts = 0, which makes the short-term event stream temporally degenerate (from frame t to frame t). Please clarify whether this is intentional or a typo, and explain how the short-term stream contributes in that case.
  2. [Section 4.3.2] The progressive training schedule for 404NotFound contains an apparent inconsistency: the text says training starts with patch size 152x152 for 250K iterations, then lists patch/batch pairs 'updated ... at iterations [250K, 200K, 150K].' This is confusing, and the initial patch size is likely a typo for 192x192 given the later mention of 'when patch size is 192.' Please rewrite the schedule to specify each stage's duration and patch size unambiguously.
  3. [Section 4.3.3] The sentence 'The results in Table 1 show that their approach yields the best performance' is ambiguous because 404NotFound ranks third in PSNR while achieving the highest SSIM. Please rephrase to say 'the highest SSIM score' or otherwise clarify which metric is meant.
  4. [Section 2.2 and Section 3.2] Since the challenge had no restrictions on computational complexity or model size, observations such as 'pretrained weights matters' and 'effective training strategies' are correlations, not controlled findings. I suggest softening 'demonstrating the advantages' to 'consistent with the advantages' and noting that the ranking reflects a joint algorithm/compute trade-off.
  5. [Section 2.2] The GitHub URL in the text appears with a space: 'https://github.com/AHupuJR/NTIRE2025 EventDeblur challenge'. Please ensure the link is correctly formatted and clickable.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the challenge ranking is a direct measurement of submitted outputs against hidden ground-truth images, with no fitted parameter, prediction-by-construction, or load-bearing self-citation loop.

full rationale

This paper is a challenge report rather than a derivation. Its central claims are the measured PSNR/SSIM values in Table 1 and the qualitative observations in Sec. 3.2. These are presented as empirical facts about submitted models evaluated on the held-out 271-image HighREV test set, with ground truth hidden from participants. There is no equation in the paper that defines an input in terms of the predicted quantity, no fitted parameter that is later renamed as a prediction, and no invocation of a uniqueness theorem or prior result that forces the ranking. The organizers' own prior works (EFNet [39], REFID [40], HighREV [40]) are used as baselines and as the dataset source, which may create a benchmark-design lineage favoring certain methods, but the paper does not claim to derive any result from those citations; the measured ranking could in principle favor any team. The integrity of the challenge relies on stated rules without independent enforcement, but that is a procedural concern, not a circularity in the logical sense. Accordingly, no circular step meeting the evidence threshold is present, and the appropriate score is 0.

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

The paper's central claim is a measured ranking, not a derivation, so it introduces no fitted parameters and no invented entities. The credibility of the measurements rests on the assumptions listed: metric validity, dataset representativeness, spatial alignment, and participant compliance with data-use rules. None of these are verified in the paper beyond assertion.

assumptions (4)
  • domain assumption PSNR and SSIM are accepted as valid quantitative measures of deblurring quality.
    All rankings in Table 1 depend on these metrics; the paper does not provide perceptual validation or human studies.
  • domain assumption HighREV provides spatially aligned event-image pairs.
    Stated in Sec 1: methods operate under the assumption that input images and events are spatially aligned. Misalignment in real use would break the conclusions.
  • domain assumption The 271-image HighREV test set is representative of the event-based deblurring task.
    The dataset statistics and scene coverage are not analyzed; the test set may be narrow, limiting generalizability.
  • domain assumption Participants followed the rule not to train on HighREV validation or test data.
    Sec 3.3 describes the prohibition but no enforcement or audit mechanism; a violation would invalidate the ranking.

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Cite this review

Pith. "Pith review of NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Results." pith.science (2026). https://pith.science/paper/KMWRNRY6

@misc{pith2026250412401,
  author       = {Pith},
  title        = {Pith review of: NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Results},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KMWRNRY6}},
  note         = {Machine review of arXiv:2504.12401}
}
read the original abstract

This paper presents an overview of NTIRE 2025 the First Challenge on Event-Based Image Deblurring, detailing the proposed methodologies and corresponding results. The primary goal of the challenge is to design an event-based method that achieves high-quality image deblurring, with performance quantitatively assessed using Peak Signal-to-Noise Ratio (PSNR). Notably, there are no restrictions on computational complexity or model size. The task focuses on leveraging both events and images as inputs for single-image deblurring. A total of 199 participants registered, among whom 15 teams successfully submitted valid results, offering valuable insights into the current state of event-based image deblurring. We anticipate that this challenge will drive further advancements in event-based vision research.

Figures

Figures reproduced from arXiv: 2504.12401 by the authors.

Figure 1
Figure 1. The model architecture of TEIDNet, proposed by Team [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. The framework of DASTF-Net, proposed by Team [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. The architecture diagram of EV-Deblurformer, proposed by Team 404NotFound, is designed for event-guided motion deblurring. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: An overview of the method proposed by Team [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: The architecture of the Bi-directional Gathered Recurrent Network (BGRN), proposed by Team Wei, is designed for event [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Architecture of DCCM, proposed by Team DVS-WHU. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 8
Figure 8. Figure 8: Overview of the proposed pipeline by Team SMU. [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]
Figure 7
Figure 7. Figure 7: The framework of Event-Image Deblurformer Network [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 9
Figure 9. Figure 9: The model framework proposed by Team JNU620. [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]
Figure 10
Figure 10. Figure 10: DEFNet architecture, proposed by Team colab. [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]
Figure 13
Figure 13. Figure 13: Left: Input blurry frame. Right: output of KUnet, with [PITH_FULL_IMAGE:figures/full_fig_p012_13.png]
Figure 11
Figure 11. Figure 11: The visualization of the motion edges [PITH_FULL_IMAGE:figures/full_fig_p012_11.png]
Figure 12
Figure 12. Figure 12: The edges in the ground truth frame 4.14. KUnet 4.14.1. Architecture Their solution is built upon a custom KUnet backbone tai￾lored for event-based image deblurring. The model employs a dual-encoder strategy that separately processes RGB im￾ages and voxelized event da…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

57 extracted references · 29 canonical work pages

  1. [1]

    Ev-segnet: Semantic segmentation for event-based cameras

    Inigo Alonso and Ana C Murillo. Ev-segnet: Semantic segmentation for event-based cameras. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0–0, 2019. 1

    work page 2019

  2. [2]

    Efficient human pose estimation via 3d event point cloud

    Jiaan Chen, Hao Shi, Yaozu Ye, Kailun Yang, Lei Sun, and Kaiwei Wang. Efficient human pose estimation via 3d event point cloud. In 2022 International Conference on 3D Vision (3DV), pages 1–10. IEEE, 2022. 1

    work page 2022

  3. [3]

    Simple baselines for image restoration

    Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, and Jian Sun. Simple baselines for image restoration. In European Con- ference on Computer Vision , pages 17–33. Springer, 2022. 5

    work page 2022

  4. [4]

    NTIRE 2025 challenge on image super-resolution (×4): Methods and results

    Zheng Chen, Kai Liu, Jue Gong, Jingkai Wang, Lei Sun, Zongwei Wu, Radu Timofte, Yulun Zhang, et al. NTIRE 2025 challenge on image super-resolution (×4): Methods and results. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Work- shops, 2025. 2

    2025

  5. [5]

    NTIRE 2025 challenge on real-world face restoration: Methods and results

    Zheng Chen, Jingkai Wang, Kai Liu, Jue Gong, Lei Sun, Zongwei Wu, Radu Timofte, Yulun Zhang, et al. NTIRE 2025 challenge on real-world face restoration: Methods and results. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Work- shops, 2025. 2

    2025

  6. [6]

    Recovering Continuous Scene Dynamics from A Single Blurry Image with Events

    Zhangyi Cheng, Xiang Zhang, Lei Yu, Jianzhuang Liu, Wen Yang, and Gui-Song Xia. Recovering continuous scene dynamics from a single blurry image with events. arXiv preprint arXiv:2304.02695, 2023. 8

    work page Pith review arXiv 2023

  7. [7]

    NTIRE 2025 challenge on raw image restoration and super-resolution

    Marcos Conde, Radu Timofte, et al. NTIRE 2025 challenge on raw image restoration and super-resolution. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2025. 2

    2025

  8. [8]

    Raw image reconstruc- tion from RGB on smartphones

    Marcos Conde, Radu Timofte, et al. Raw image reconstruc- tion from RGB on smartphones. NTIRE 2025 challenge re- port. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR) Workshops ,

    2025

Show all 57 references
  1. [9]

    Selective frequency network for image restoration

    Yuning Cui, Yi Tao, Zhenshan Bing, Wenqi Ren, Xinwei Gao, Xiaochun Cao, Kai Huang, and Alois Knoll. Selective frequency network for image restoration. In International Conference on Learning Representations, 2023. 10

    2023

  2. [10]

    NTIRE 2025 challenge on night photography rendering

    Egor Ershov, Sergey Korchagin, Alexei Khalin, Artyom Pan- shin, Arseniy Terekhin, Ekaterina Zaychenkova, Georgiy Lobarev, Vsevolod Plokhotnyuk, Denis Abramov, Elisey Zhdanov, Sofia Dorogova, Yasin Mamedov, Nikola Banic, Georgii Perevozchikov, Radu Timofte, et al. NTIRE 2025 ch...

    2025

  3. [11]

    NTIRE 2025 challenge on cross-domain few-shot object detection: Methods and results

    Yuqian Fu, Xingyu Qiu, Bin Ren Yanwei Fu, Radu Timofte, Nicu Sebe, Ming-Hsuan Yang, Luc Van Gool, et al. NTIRE 2025 challenge on cross-domain few-shot object detection: Methods and results. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition ...

    2025

  4. [12]

    Event-based vision: A survey

    Guillermo Gallego, Tobi Delbruck, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew Davison, J ¨org Conradt, Kostas Daniilidis, and Da- vide Scaramuzza. Event-based vision: A survey. IEEE Trans. Pattern Anal. Mach. Intell., 44(1):154–180, 2022. 1

    2022

  5. [13]

    NTIRE 2025 challenge on text to image generation model quality assess- ment

    Shuhao Han, Haotian Fan, Fangyuan Kong, Wenjie Liao, Chunle Guo, Chongyi Li, Radu Timofte, et al. NTIRE 2025 challenge on text to image generation model quality assess- ment. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR) Workshops ,

    2025

  6. [14]

    Pan- mamba: Effective pan-sharpening with state space model

    Xuanhua He, Ke Cao, Jie Zhang, Keyu Yan, Yingying Wang, Rui Li, Chengjun Xie, Danfeng Hong, and Man Zhou. Pan- mamba: Effective pan-sharpening with state space model. Information Fusion, 115:102779, 2025. 7

    2025

  7. [15]

    v2e: From video frames to realistic dvs events

    Yuhuang Hu, Shih-Chii Liu, and Tobi Delbruck. v2e: From video frames to realistic dvs events. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1312–1321, 2021. 8

    2021

  8. [16]

    NTIRE 2025 challenge on video quality enhancement for video con- ferencing: Datasets, methods and results

    Varun Jain, Zongwei Wu, Quan Zou, Louis Florentin, Hen- rik Turbell, Sandeep Siddhartha, Radu Timofte, et al. NTIRE 2025 challenge on video quality enhancement for video con- ferencing: Datasets, methods and results. In Proceedings of the IEEE/CVF Conference on Computer Vision...

    2025

  9. [17]

    Event-based video deblur- ring based on image and event feature fusion.Expert Systems with Applications, 223:119917, 2023

    J Kim, D K Ghosh, and Y J Jung. Event-based video deblur- ring based on image and event feature fusion.Expert Systems with Applications, 223:119917, 2023. 10

    2023

  10. [18]

    Frequency- aware event-based video deblurring for real-world motion blur

    Taewoo Kim, Hoonhee Cho, and Kuk-Jin Yoon. Frequency- aware event-based video deblurring for real-world motion blur. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition , pages 24966–24976,

  11. [19]

    NTIRE 2025 challenge on efficient burst hdr and restoration: Datasets, methods, and results

    Sangmin Lee, Eunpil Park, Angel Canelo, Hyunhee Park, Youngjo Kim, Hyungju Chun, Xin Jin, Chongyi Li, Chun-Le Guo, Radu Timofte, et al. NTIRE 2025 challenge on efficient burst hdr and restoration: Datasets, methods, and results. In Proceedings of the IEEE/CVF Conference on Com...

    2025

  12. [20]

    A coarse-to-fine fusion network for event-based image deblurring

    Huan Li, Hailong Shi, and Xingyu Gao. A coarse-to-fine fusion network for event-based image deblurring. In Pro- ceedings of the International Joint Conference on Artificial Intelligence, pages 974–982, 2024. 9, 10

    2024

  13. [21]

    NTIRE 2025 challenge on day and night raindrop removal for dual-focused images: Methods and results

    Xin Li, Yeying Jin, Xin Jin, Zongwei Wu, Bingchen Li, Yufei Wang, Wenhan Yang, Yu Li, Zhibo Chen, Bihan Wen, Robby Tan, Radu Timofte, et al. NTIRE 2025 challenge on day and night raindrop removal for dual-focused images: Methods and results. In Proceedings of the IEEE/CVF Conf...

    2025

  14. [22]

    NTIRE 2025 challenge on short-form ugc video quality assessment and enhancement: Kwaisr dataset and study

    Xin Li, Xijun Wang, Bingchen Li, Kun Yuan, Yizhen Shao, Suhang Yao, Ming Sun, Chao Zhou, Radu Timofte, and Zhibo Chen. NTIRE 2025 challenge on short-form ugc video quality assessment and enhancement: Kwaisr dataset and study. In Proceedings of the IEEE/CVF Conference on Com- p...

    2025

  15. [23]

    NTIRE 2025 challenge on short-form ugc video quality assessment and enhancement: Methods and results

    Xin Li, Kun Yuan, Bingchen Li, Fengbin Guan, Yizhen Shao, Zihao Yu, Xijun Wang, Yiting Lu, Wei Luo, Suhang Yao, Ming Sun, Chao Zhou, Zhibo Chen, Radu Timofte, et al. NTIRE 2025 challenge on short-form ugc video quality assessment and enhancement: Methods and results. In Pro- c...

    2025

  16. [24]

    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,

  17. [25]

    NTIRE 2025 the 2nd restore any image model (RAIM) in the wild challenge

    Jie Liang, Radu Timofte, Qiaosi Yi, Zhengqiang Zhang, Shuaizheng Liu, Lingchen Sun, Rongyuan Wu, Xindong Zhang, Hui Zeng, Lei Zhang, et al. NTIRE 2025 the 2nd restore any image model (RAIM) in the wild challenge. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion a...

    2025

  18. [26]

    Event-conditioned dual-modal fusion for motion deblurring

    Kean Liu, Mingchen Zhong, Senyan Xu, Zhijing Sun, Jiay- ing Zhu, Chengjie Ge, Xin Lu, Xingbo Wang, Xueyang Fu, and Zheng-Jun Zha. Event-conditioned dual-modal fusion for motion deblurring. InProceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition (C...

    2025

  19. [27]

    NTIRE 2025 XGC quality assessment chal- lenge: Methods and results

    Xiaohong Liu, Xiongkuo Min, Qiang Hu, Xiaoyun Zhang, Jie Guo, et al. NTIRE 2025 XGC quality assessment chal- lenge: Methods and results. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2025. 2

    2025

  20. [28]

    NTIRE 2025 challenge on low light image enhancement: Methods and results

    Xiaoning Liu, Zongwei Wu, Florin-Alexandru Vasluianu, Hailong Yan, Bin Ren, Yulun Zhang, Shuhang Gu, Le Zhang, Ce Zhu, Radu Timofte, et al. NTIRE 2025 challenge on low light image enhancement: Methods and results. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion ...

    2025

  21. [29]

    Decoupled weight de- cay regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight de- cay regularization. In International Conference on Learning Representations, 2019. 7, 10

    2019

  22. [30]

    Sge: structured light system based on gray code with an event camera

    Xingyu Lu, Lei Sun, Diyang Gu, and Kaiwei Wang. Sge: structured light system based on gray code with an event camera. Optics Express, 32(26):46044–46061, 2024. 1

    2024

  23. [31]

    Adarevd: Adap- tive patch exiting reversible decoder pushes the limit of im- age deblurring

    Xintian Mao, Qingli Li, and Yan Wang. Adarevd: Adap- tive patch exiting reversible decoder pushes the limit of im- age deblurring. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 25681– 25690, 2024. 10

    2024

  24. [32]

    Multi-bracket high dynamic range imaging with event cameras

    Nico Messikommer, Stamatios Georgoulis, Daniel Gehrig, Stepan Tulyakov, Julius Erbach, Alfredo Bochicchio, Yuanyou Li, and Davide Scaramuzza. Multi-bracket high dynamic range imaging with event cameras. In Proceedings of the IEEE/CVF conference on computer vision and pattern r...

    2022

  25. [33]

    Data-driven feature tracking for event cameras

    Nico Messikommer, Carter Fang, Mathias Gehrig, and Da- vide Scaramuzza. Data-driven feature tracking for event cameras. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 5642– 5651, 2023. 9

    2023

  26. [34]

    Esl: Event-based structured light

    Manasi Muglikar, Guillermo Gallego, and Davide Scara- muzza. Esl: Event-based structured light. In 2021 Inter- national Conference on 3D Vision (3DV), pages 1165–1174. IEEE, 2021. 1

    2021

  27. [35]

    Ntire 2019 challenge on video deblurring and super- resolution: Dataset and study

    Seungjun Nah, Sungyong Baik, Seokil Hong, Gyeongsik Moon, Sanghyun Son, Radu Timofte, and Kyoung Mu Lee. Ntire 2019 challenge on video deblurring and super- resolution: Dataset and study. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition work...

    2019

  28. [36]

    The tenth NTIRE 2025 efficient super- resolution challenge report

    Bin Ren, Hang Guo, Lei Sun, Zongwei Wu, Radu Timo- fte, Yawei Li, et al. The tenth NTIRE 2025 efficient super- resolution challenge report. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2025. 2

    2025

  29. [37]

    NTIRE 2025 challenge on UGC video enhancement: Meth- ods and results

    Nickolay Safonov, Alexey Bryntsev, Andrey Moskalenko, Dmitry Kulikov, Dmitriy Vatolin, Radu Timofte, et al. NTIRE 2025 challenge on UGC video enhancement: Meth- ods and results. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Work- s...

    2025

  30. [38]

    Reducing the sim-to-real gap for event cam- eras

    Timo Stoffregen, Cedric Scheerlinck, Davide Scaramuzza, Tom Drummond, Nick Barnes, Lindsay Kleeman, and Robert Mahony. Reducing the sim-to-real gap for event cam- eras. In European Conference on Computer Vision , pages 534–549, 2020. 8

    2020

  31. [39]

    Event-based fusion for motion deblurring with cross- modal attention

    Lei Sun, Christos Sakaridis, Jingyun Liang, Qi Jiang, Kailun Yang, Peng Sun, Yaozu Ye, Kaiwei Wang, and Luc Van Gool. Event-based fusion for motion deblurring with cross- modal attention. In European Conference on Computer Vi- sion, pages 412–428. Springer, 2022. 1, 3, 4, 6, 7...

    2022

  32. [40]

    Event-based frame interpolation with ad-hoc de- blurring

    Lei Sun, Christos Sakaridis, Jingyun Liang, Peng Sun, Jiezhang Cao, Kai Zhang, Qi Jiang, Kaiwei Wang, and Luc Van Gool. Event-based frame interpolation with ad-hoc de- blurring. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 18043–...

    2023

  33. [41]

    A unified framework for event-based frame interpolation with ad-hoc deblurring in the wild

    Lei Sun, Daniel Gehrig, Christos Sakaridis, Mathias Gehrig, Jingyun Liang, Peng Sun, Zhijie Xu, Kaiwei Wang, Luc Van Gool, and Davide Scaramuzza. A unified framework for event-based frame interpolation with ad-hoc deblurring in the wild. IEEE Transactions on Pattern Analysis a...

    2024

  34. [42]

    NTIRE 2025 challenge on event-based image deblurring: Methods and results

    Lei Sun, Andrea Alfarano, Peiqi Duan, Shaolin Su, Kaiwei Wang, Boxin Shi, Radu Timofte, Danda Pani Paudel, Luc Van Gool, et al. NTIRE 2025 challenge on event-based image deblurring: Methods and results. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern R...

    2025

  35. [43]

    Low-light image enhancement using event-based illumination estimation

    Lei Sun, Yuhan Bao, Jiajun Zhai, Jingyun Liang, Yu- lun Zhang, Kaiwei Wang, Danda Pani Paudel, and Luc Van Gool. Low-light image enhancement using event-based illumination estimation. arXiv preprint arXiv:2504.09379 ,

    arXiv

  36. [44]

    The tenth ntire 2025 image denoising challenge report

    Lei Sun, Hang Guo, Bin Ren, Luc Van Gool, Radu Timo- fte, Yawei Li, et al. The tenth ntire 2025 image denoising challenge report. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR) Workshops, 2025. 2

    2025

  37. [45]

    Motion aware event representation-driven image deblurring

    Zhijing Sun, Xueyang Fu, Longzhuo Huang, Aiping Liu, and Zheng-Jun Zha. Motion aware event representation-driven image deblurring. In European Conference on Computer Vi- sion, pages 418–435. Springer, 2024. 4

    2024

  38. [46]

    NTIRE 2025 image shadow removal challenge report

    Florin-Alexandru Vasluianu, Tim Seizinger, Zhuyun Zhou, Cailian Chen, Zongwei Wu, Radu Timofte, et al. NTIRE 2025 image shadow removal challenge report. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2025. 2

    2025

  39. [47]

    NTIRE 2025 ambi- ent lighting normalization challenge

    Florin-Alexandru Vasluianu, Tim Seizinger, Zhuyun Zhou, Zongwei Wu, Radu Timofte, et al. NTIRE 2025 ambi- ent lighting normalization challenge. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2025. 2

    2025

  40. [48]

    NTIRE 2025 challenge on light field image super-resolution: Methods and results

    Yingqian Wang, Zhengyu Liang, Fengyuan Zhang, Lvli Tian, Longguang Wang, Juncheng Li, Jungang Yang, Radu Timofte, Yulan Guo, et al. NTIRE 2025 challenge on light field image super-resolution: Methods and results. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision a...

    2025

  41. [49]

    Image quality assessment: from error visibility to structural similarity

    Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Si- moncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Process- ing, 13(4):600–612, 2004. 5

    2004

  42. [50]

    Event- based blurry frame interpolation under blind exposure

    Wenming Weng, Yueyi Zhang, and Zhiwei Xiong. Event- based blurry frame interpolation under blind exposure. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 1588–1598, 2023. 6

    2023

  43. [51]

    NTIRE 2025 challenge on single image reflection removal in the wild: Datasets, methods and results

    Kangning Yang, Jie Cai, Ling Ouyang, Florin-Alexandru Vasluianu, Radu Timofte, Jiaming Ding, Huiming Sun, Lan Fu, Jinlong Li, Chiu Man Ho, Zibo Meng, et al. NTIRE 2025 challenge on single image reflection removal in the wild: Datasets, methods and results. In Proceedings of th...

    2025

  44. [52]

    Motion deblurring via spatial-temporal collaboration of frames and events

    Wen Yang, Jinjian Wu, Jupo Ma, Leida Li, and Guangming Shi. Motion deblurring via spatial-temporal collaboration of frames and events. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6531–6539, 2024. 6

    2024

  45. [53]

    NTIRE 2025 challenge on hr depth from images of specular and transparent surfaces

    Pierluigi Zama Ramirez, Fabio Tosi, Luigi Di Stefano, Radu Timofte, Alex Costanzino, Matteo Poggi, Samuele Salti, Ste- fano Mattoccia, et al. NTIRE 2025 challenge on hr depth from images of specular and transparent surfaces. In Pro- ceedings of the IEEE/CVF Conference on Compu...

    2025

  46. [54]

    Learning enriched features for real image restoration and enhancement

    Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Learning enriched features for real image restoration and enhancement. In European Conference on Computer Vi- sion, pages 492–511. Springer, 2020. 5

    2020

  47. [55]

    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,

  48. [56]

    A multi-scale re- current framework for motion segmentation with event cam- era

    Shaobo Zhang, Lei Sun, and Kaiwei Wang. A multi-scale re- current framework for motion segmentation with event cam- era. IEEE Access, 11:80105–80114, 2023. 1

    2023

  49. [57]

    Residual dense network for image super-resolution

    Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu. Residual dense network for image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2472–2481, 2018. 7

    2018

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.