NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results

This paper reviews the NTIRE2021 challenge on burst super-resolution. Given a RAW noisy burst as input, the task in the challenge was to generate a clean RGB image with 4 times higher resolution. The challenge contained two tracks; Track 1 evaluating on synthetically generated data, and Track 2 using real-world bursts from mobile camera. In the final testing phase, 6 teams submitted results using a diverse set of solutions. The top-performing methods set a new state-of-the-art for the burst super-resolution task.

[1]  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).

[2]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

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

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

[5]  Brendt Wohlberg,et al.  Plug-and-Play priors for model based reconstruction , 2013, 2013 IEEE Global Conference on Signal and Information Processing.

[6]  Wojciech Samek,et al.  Multi-Kernel Prediction Networks for Denoising of Burst Images , 2019, 2019 IEEE International Conference on Image Processing (ICIP).

[7]  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.

[8]  Jenq-Neng Hwang,et al.  NTIRE 2021 Multi-modal Aerial View Object Classification Challenge , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[9]  Xin Yu,et al.  Ultra-Resolving Face Images by Discriminative Generative Networks , 2016, ECCV.

[10]  Radu Timofte,et al.  NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

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

[12]  L. Gool,et al.  SRFlow: Learning the Super-Resolution Space with Normalizing Flow , 2020, ECCV.

[13]  Xiaohan Chen,et al.  Plug-and-Play Methods Provably Converge with Properly Trained Denoisers , 2019, ICML.

[14]  Luc Van Gool,et al.  Replacing Mobile Camera ISP with a Single Deep Learning Model , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[15]  Abhinav Gupta,et al.  Non-local Neural Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[16]  Jie Liu,et al.  Residual Feature Distillation Network for Lightweight Image Super-Resolution , 2020, ECCV Workshops.

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

[18]  Narendra Ahuja,et al.  Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Alexei A. Efros,et al.  The Unreasonable Effectiveness of Deep Features as a Perceptual Metric , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[20]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Stanley H. Chan,et al.  Plug-and-Play ADMM for Image Restoration: Fixed-Point Convergence and Applications , 2016, IEEE Transactions on Computational Imaging.

[22]  Jie Li,et al.  Channel-Wise and Spatial Feature Modulation Network for Single Image Super-Resolution , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[23]  Marc Levoy,et al.  Handheld multi-frame super-resolution , 2019, ACM Trans. Graph..

[24]  Radu Timofte,et al.  NTIRE 2021 Challenge on Video Super-Resolution , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[25]  Qin Xu,et al.  Learning Raw Image Denoising With Bayer Pattern Unification and Bayer Preserving Augmentation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[26]  Haoqiang Fan,et al.  EBSR: Feature Enhanced Burst Super-Resolution with Deformable Alignment , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[27]  Jonathan T. Barron,et al.  Unprocessing Images for Learned Raw Denoising , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Li Fei-Fei,et al.  Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.

[29]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.

[30]  D. Hunter,et al.  A Tutorial on MM Algorithms , 2004 .

[31]  Stamatios Lefkimmiatis,et al.  Iterative Joint Image Demosaicking and Denoising Using a Residual Denoising Network , 2018, IEEE Transactions on Image Processing.

[32]  Ling Shao,et al.  NTIRE 2021 NonHomogeneous Dehazing Challenge Report , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[33]  Kun Gao,et al.  NTIRE 2021 Learning the Super-Resolution Space Challenge , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[34]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[35]  Radu Timofte,et al.  NTIRE 2021 Challenge on Perceptual Image Quality Assessment , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[36]  Radu Timofte,et al.  NTIRE 2021 Challenge on Image Deblurring , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[37]  Daniel Rueckert,et al.  Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Luc Van Gool,et al.  Deep Burst Super-Resolution , 2021 .

[39]  Javier Sánchez The Inverse Compositional Algorithm for Parametric Registration , 2016, Image Process. Line.

[40]  Xiaoou Tang,et al.  Learning a Deep Convolutional Network for Image Super-Resolution , 2014, ECCV.

[41]  Radu Timofte,et al.  NTIRE 2021 Depth Guided Image Relighting Challenge , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[42]  Luc Van Gool,et al.  Deep Unfolding Network for Image Super-Resolution , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[43]  Fahad Shahbaz Khan,et al.  NTIRE 2021 Challenge for Defocus Deblurring Using Dual-pixel Images: Methods and Results , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[44]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[45]  Tengyu Ma,et al.  Fixup Initialization: Residual Learning Without Normalization , 2019, ICLR.

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

[47]  Chuan-Kuei Huang,et al.  Multi chaotic systems based pixel shuffle for image encryption , 2009 .

[48]  Jian Yang,et al.  Image Super-Resolution via Deep Recursive Residual Network , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[49]  Jonathan T. Barron,et al.  Burst Denoising with Kernel Prediction Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[50]  Wooyeong Cho,et al.  Weighted Multi-Kernel Prediction Network for Burst Image Super-Resolution , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[51]  Jean Ponce,et al.  Aliasing is your Ally: End-to-End Super-Resolution from Raw Image Bursts , 2021, ArXiv.

[52]  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).

[53]  Radu Timofte,et al.  NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[54]  Gregory Shakhnarovich,et al.  Recurrent Back-Projection Network for Video Super-Resolution , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[55]  Stephen J. Wright,et al.  Numerical Optimization (Springer Series in Operations Research and Financial Engineering) , 2000 .

[56]  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).

[57]  Zixiang Xiong,et al.  Attention Mechanism Enhanced Kernel Prediction Networks for Denoising of Burst Images , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[58]  Gian Luca Foresti,et al.  Deep Iterative Residual Convolutional Network for Single Image Super-Resolution , 2020, 2020 25th International Conference on Pattern Recognition (ICPR).

[59]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[60]  Donald Geman,et al.  Nonlinear image recovery with half-quadratic regularization , 1995, IEEE Trans. Image Process..