NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results

This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results. A new DIVerse 2K resolution image dataset (DIV2K) was employed. The challenge had 6 competitions divided into 2 tracks with 3 magnification factors each. Track 1 employed the standard bicubic downscaling setup, while Track 2 had unknown downscaling operators (blur kernel and decimation) but learnable through low and high res train images. Each competition had ∽100 registered participants and 20 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.

Luc Van Gool | Eirikur Agustsson | Radu Timofte | Lei Cao | Yu Zhao | Thomas S. Huang | Jae-Seok Choi | Munchurl Kim | Ding Liu | Karen O. Egiazarian | Dacheng Tao | Liang Lin | Deqing Sun | Ming-Hsuan Yang | Vishal Monga | Wangmeng Zuo | Ye Duan | Jong Chul Ye | Xueying Qin | Xuan-Phung Huynh | Qi Guo | Xu Lin | Honghui Shi | Lei Zhang | Yapeng Tian | Zhangyang Wang | Yu Qiao | Vladimir Katkovnik | Kai Zhang | Yunjin Chen | Kyoung Mu Lee | Tiep Huu Vu | Hojjat Seyed Mousavi | Chao Dong | Chen Change Loy | Ruxin Wang | Zhimin Tang | Wei Han | Xinchao Wang | Xiangyu Xu | Shaohui Li | Yuchao Dai | Jiahui Yu | Ke Yu | Arnav Kumar Jain | Yoseob Han | Haichao Yu | Min Fu | Yulun Zhang | Cristóvão Cruz | Jianxin Pang | Sanghyun Son | Rakesh Mehta | Linkai Luo | Tiantong Guo | Woong Bae | Giang Bui | Yuchen Fan | Jae Jun Yoo | Xibin Song | Zhengtao Wang | Jin-shan Pan | Yujin Zhang | Ruofan Zhou | Truc Le | Bee Lim | Heewon Kim | Seungjun Nah | Xintao Wang | Shixiang Wu | Wen Heng | Jinchang Xu | Abhinav Agarwalla | Ch V. Sai Praveen | Hongdiao Wen | Che Zhu | Zhiqiang Xia | J. C. Ye | Thomas S. Huang | L. Gool | Ming-Hsuan Yang | Yujin Zhang | Bee Lim | Sanghyun Son | Heewon Kim | Seungjun Nah | Y. Qiao | V. Katkovnik | K. Egiazarian | Deqing Sun | Chao Dong | Liang Lin | D. Tao | Xibin Song | R. Timofte | E. Agustsson | Lei Zhang | Xintao Wang | Yapeng Tian | K. Yu | Yulun Zhang | Shixiang Wu | Woong Bae | J. Yoo | Yoseob Han | Jae-Seok Choi | Munchurl Kim | Yuchen Fan | Jiahui Yu | Wei Han | Ding Liu | Haichao Yu | Zhangyang Wang | Humphrey Shi | Xinchao Wang | Yunjin Chen | K. Zhang | W. Zuo | Zhimin Tang | Linkai Luo | Shaohui Li | Min Fu | Lei Cao | Wen Heng | Giang Bui | Truc Le | Y. Duan | Ruxin Wang | Xu Lin | Jianxin Pang | Jinchang Xu | Yu Zhao | Xiangyu Xu | Jin-shan Pan | Yuchao Dai | Xueying Qin | X. Huynh | Tiantong Guo | H. Mousavi | T. Vu | V. Monga | Cristóvão Cruz | Rakesh Mehta | A. Jain | Abhinav Agarwalla | C. Praveen | Ruofan Zhou | Hongdiao Wen | Chen Zhu | Zhiqiang Xia | Zhengtao Wang | Qi Guo | T. Huang

[1]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

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

[3]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[4]  Thomas S. Huang,et al.  Image super-resolution as sparse representation of raw image patches , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Li Xu,et al.  Two-Phase Kernel Estimation for Robust Motion Deblurring , 2010, ECCV.

[6]  Michael Elad,et al.  On Single Image Scale-Up Using Sparse-Representations , 2010, Curves and Surfaces.

[7]  Aline Roumy,et al.  Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding , 2012, BMVC.

[8]  Luc Van Gool,et al.  Anchored Neighborhood Regression for Fast Example-Based Super-Resolution , 2013, 2013 IEEE International Conference on Computer Vision.

[9]  Franco Scarselli,et al.  On the Complexity of Neural Network Classifiers: A Comparison Between Shallow and Deep Architectures , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[10]  Chih-Yuan Yang,et al.  Single-Image Super-Resolution: A Benchmark , 2014, ECCV.

[11]  Luc Van Gool,et al.  A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution , 2014, ACCV.

[12]  Leslie N. Smith,et al.  No More Pesky Learning Rate Guessing Games , 2015, ArXiv.

[13]  Thomas S. Huang,et al.  Deep Networks for Image Super-Resolution with Sparse Prior , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[14]  Dacheng Tao,et al.  Multi-View Intact Space Learning , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[16]  Trevor Darrell,et al.  Fully convolutional networks for semantic segmentation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[18]  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).

[19]  Xiaoou Tang,et al.  Depth Map Super-Resolution by Deep Multi-Scale Guidance , 2016, ECCV.

[20]  Guillermo Sapiro,et al.  Deep Video Deblurring , 2016, ArXiv.

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

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

[23]  Jian Sun,et al.  Identity Mappings in Deep Residual Networks , 2016, ECCV.

[24]  Xiaoou Tang,et al.  Accelerating the Super-Resolution Convolutional Neural Network , 2016, ECCV.

[25]  Jia Deng,et al.  Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.

[26]  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).

[27]  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).

[28]  Joan Bruna,et al.  Super-Resolution with Deep Convolutional Sufficient Statistics , 2015, ICLR.

[29]  Vincent Dumoulin,et al.  Deconvolution and Checkerboard Artifacts , 2016 .

[30]  Yu Zhao,et al.  Fast and Accurate Image Super-Resolution Using a Combined Loss , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[31]  Lei Zhang,et al.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.

[32]  Vishal Monga,et al.  Sparsity-Based Color Image Super Resolution via Exploiting Cross Channel Constraints , 2016, IEEE Transactions on Image Processing.

[33]  Jae-Seok Choi,et al.  A Deep Convolutional Neural Network with Selection Units for Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[34]  Vishal Monga,et al.  Deep Wavelet Prediction for Image Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[35]  Jong Chul Ye,et al.  Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[36]  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).

[37]  Thomas S. Huang,et al.  Balanced Two-Stage Residual Networks for Image Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[38]  Eirikur Agustsson,et al.  NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[39]  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).

[40]  Bernhard Schölkopf,et al.  EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[41]  Karen O. Egiazarian,et al.  Single Image Super-Resolution Based on Wiener Filter in Similarity Domain , 2017, IEEE Transactions on Image Processing.