NTIRE 2019 Challenge on Video Deblurring: Methods and Results

Nah, Seungjun; Timofte, Radu; Baik, Seung‐Chul; Hong, Seung Hwa; Moon, Gyeongsik; Son, Sanghyun; Lee, Kyoung Mu; Wang, Xintao; Chan, Kelvin C. K.; Yu, Ke; Dong, Chao; Loy, Chen Change; Yi, Fan; Yu, Jiahui; Liu, Ding; Huang, Thomas S.; Sim, Hyeonjun; Kim, Munchurl; Park, Dong-Won; Kim, Ji-Soo; Chun, Se Young; Haris, Muhammad; Shakhnarovich, Greg; Ukita, Norimichi; Zamir, Syed Waqas; Arora, Aditya; Khan, Salman; Khan, Fahad Shahbaz; Shao, Ling; Gupta, Rahul; Chudasama, Vishal; Patel, Heena; Upla, Kishor P.; Fan, Hongfei; Guo, Li; Zhang, Yumei; Li, Xiang; Zhang, Wenjie; He, Qingwen; Purohit, Kuldeep; Rajagopalan, A. N.; Kim, Jeonghun; Tofighi, Mohammad; Guo, Tiantong; Monga, Vishal

doi:10.1109/cvprw.2019.00249

Cited by 33 publications

(13 citation statements)

References 19 publications

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“…We participated in all the four tracks in the NTIRE19 video restoration and enhancement challenges [29,28]. Quantitative results are presented in Table 5.…”

Section: Evaluation On Reds Datasetmentioning

confidence: 99%

EDVR: Video Restoration With Enhanced Deformable Convolutional Networks

Wang

Chan

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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949

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Video restoration tasks, including super-resolution, deblurring, etc, are drawing increasing attention in the computer vision community. A challenging benchmark named REDS is released in the NTIRE19 Challenge. This new benchmark challenges existing methods from two aspects:(1) how to align multiple frames given large motions, and (2) how to effectively fuse different frames with diverse motion and blur. In this work, we propose a novel Video Restoration framework with Enhanced Deformable convolutions, termed EDVR, to address these challenges. First, to handle large motions, we devise a Pyramid, Cascading and Deformable (PCD) alignment module, in which frame alignment is done at the feature level using deformable convolutions in a coarse-to-fine manner. Second, we propose a Temporal and Spatial Attention (TSA) fusion module, in which attention is applied both temporally and spatially, so as to emphasize important features for subsequent restoration. Thanks to these modules, our EDVR wins the champions and outperforms the second place by a large margin in all four tracks in the NTIRE19 video restoration and enhancement challenges. EDVR also demonstrates superior performance to state-of-the-art published methods on video super-resolution and deblurring. The code is available at https://github.com/xinntao/EDVR.

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“…We participated in all the four tracks in the NTIRE19 video restoration and enhancement challenges [29,28]. Quantitative results are presented in Table 5.…”

Section: Evaluation On Reds Datasetmentioning

confidence: 99%

EDVR: Video Restoration With Enhanced Deformable Convolutional Networks

Wang

Chan

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Self Cite

949

1,017

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“…External optical flow estimation or warping was employed in none of the submitted methods. TTI used a recurrent model inspired from (DBPN [6] [18]. Runtime / efficiency In Fig.…”

Section: Challenge Resultsmentioning

confidence: 99%

NTIRE 2019 Challenge on Video Super-Resolution: Methods and Results

Nah

Timofte²,

Gu³

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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This paper reviews the first NTIRE challenge on video super-resolution (restoration of rich details in lowresolution video frames) with focus on proposed solutions and results. A new REalistic and Diverse Scenes dataset (REDS) was employed. The challenge was divided into 2 tracks. Track 1 employed standard bicubic downscaling setup while Track 2 had realistic dynamic motion blurs. Each competition had 124 and 104 registered participants. There were total 14 teams in the final testing phase. They gauge the state-of-the-art in video super-resolution.

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“…As the winner of NTIRE 2019 Video Super-Resolution and Deblurring Challenges [43,42,37], EDVR [58] combines several novel components such as PCD alignment and TSA fusion to achieve state-of-the-art reconstruction quality. Recent methods adopt various techniques such as recurrent structure-detail network [16], multi-correspondence aggregation [25], temporal group attention [17], or temporally deformable alignement [57].…”

Section: Related Workmentioning

confidence: 99%

NTIRE 2021 Challenge on Video Super-Resolution

Son¹,

Lee²,

Nah³

et al. 2021

Preprint

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Super-Resolution (SR) is a fundamental computer vision task that aims to obtain a high-resolution clean image from the given low-resolution counterpart. This paper reviews the NTIRE 2021 Challenge on Video Super-Resolution. We present evaluation results from two competition tracks as well as the proposed solutions. Track 1 aims to develop conventional video SR methods focusing on the restoration quality. Track 2 assumes a more challenging environment with lower frame rates, casting spatio-temporal SR problem. In each competition, 247 and 223 participants have registered, respectively. During the final testing phase, 14 teams competed in each track to achieve state-of-the-art performance on video SR tasks.

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NTIRE 2019 Challenge on Video Deblurring: Methods and Results

Cited by 33 publications

References 19 publications

EDVR: Video Restoration With Enhanced Deformable Convolutional Networks

EDVR: Video Restoration With Enhanced Deformable Convolutional Networks

NTIRE 2019 Challenge on Video Super-Resolution: Methods and Results

NTIRE 2021 Challenge on Video Super-Resolution

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