NTIRE 2019 Challenge on Video Deblurring: Methods and Results

This paper reviews the first NTIRE challenge on video deblurring (restoration of rich details and high frequency components from blurred video frames) with focus on the proposed solutions and results. A new REalistic and Diverse Scenes dataset (REDS) was employed. The challenge was divided into 2 tracks. Track 1 employed dynamic motion blurs while Track 2 had additional MPEG video compression artifacts. Each competition had 109 and 93 registered participants. Total 13 teams competed in the final testing phase. They gauge the state-of-the-art in video deblurring problem.

[1]  Chen Change Loy,et al.  EDVR: Video Restoration With Enhanced Deformable Convolutional Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[2]  Yun Fu,et al.  Residual Dense Network for Image Super-Resolution , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[3]  Chao Dong,et al.  Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[4]  Jan Kautz,et al.  PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[5]  Yun Fu,et al.  Image Super-Resolution Using Very Deep Residual Channel Attention Networks , 2018, ECCV.

[6]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[7]  Yi Wang,et al.  Scale-Recurrent Network for Deep Image Deblurring , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[8]  Bernhard Schölkopf,et al.  Spatio-Temporal Transformer Network for Video Restoration , 2018, ECCV.

[9]  Bernhard Schölkopf,et al.  Online Video Deblurring via Dynamic Temporal Blending Network , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[10]  Xiaoou Tang,et al.  LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[11]  Ning Xu,et al.  Wide Activation for Efficient and Accurate Image Super-Resolution , 2018, ArXiv.

[12]  Chenliang Xu,et al.  TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution , 2018, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Kyoung Mu Lee,et al.  Enhanced Deep Residual Networks for Single Image Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[14]  Yu Qiao,et al.  ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks , 2018, ECCV Workshops.

[15]  Jian Sun,et al.  Guided Image Filtering , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Feng Liu,et al.  Video Frame Interpolation via Adaptive Separable Convolution , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[17]  Kyoung Mu Lee,et al.  Accurate Image Super-Resolution Using Very Deep Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Gregory Shakhnarovich,et al.  Deep Back-Projection Networks for Super-Resolution , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[19]  Lei Zhang,et al.  Extreme Channel Prior Embedded Network for Dynamic Scene Deblurring , 2019, ArXiv.

[20]  Luc Van Gool,et al.  Seven Ways to Improve Example-Based Single Image Super Resolution , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Guillermo Sapiro,et al.  Deep Video Deblurring for Hand-Held Cameras , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Yi Li,et al.  Deformable Convolutional Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[23]  Robert L. Stevenson,et al.  Dynamic range improvement through multiple exposures , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[24]  A. Murat Tekalp,et al.  NTIRE 2019 Challenge on Video Super-Resolution: Methods and Results , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[25]  Bernhard Schölkopf,et al.  Recording and Playback of Camera Shake: Benchmarking Blind Deconvolution with a Real-World Database , 2012, ECCV.

[26]  Bernhard Schölkopf,et al.  Learning Blind Motion Deblurring , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[27]  In-So Kweon,et al.  CBAM: Convolutional Block Attention Module , 2018, ECCV.

[28]  Dongwon Park,et al.  Down-Scaling with Learned Kernels in Multi-Scale Deep Neural Networks for Non-Uniform Single Image Deblurring , 2019, ArXiv.

[29]  Radu Timofte,et al.  NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[30]  Tae Hyun Kim,et al.  Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).