AIM 2019 Challenge on Image Extreme Super-Resolution: Methods and Results

This paper reviews the AIM 2019 challenge on extreme image super-resolution, the problem of restoring of rich details in a low resolution image. Compared to previous, this challenge focuses on an extreme upscaling factor, ×16, and employs the novel DIVerse 8K resolution (DIV8K) dataset. This report focuses on the proposed solutions and final results. The challenge had 2 tracks. The goal in Track 1 was to generate a super-resolution result with high fidelity, using the conventional PSNR as the primary metric to evaluate different methods. Track 2 instead focused on generating visually more pleasant super-resolution results, evaluated using subjective opinions. The two tracks had 71 and 52 registered participants, respectively, and 9 teams competed in the final testing phase. This report gauges the experimental protocol and baselines for the extreme image super-resolution task.

[1]  Lihi Zelnik-Manor,et al.  The Contextual Loss for Image Transformation with Non-Aligned Data , 2018, ECCV.

[2]  Luc Van Gool,et al.  PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report , 2018, ECCV Workshops.

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

[4]  Chen Hong,et al.  NTIRE 2019 Challenge on Real Image Super-Resolution: Methods and Results , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[5]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[6]  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).

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

[8]  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).

[9]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[10]  Radu Timofte,et al.  DIV8K: DIVerse 8K Resolution Image Dataset , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

[11]  Gregory Shakhnarovich,et al.  Deep Back-ProjectiNetworks for Single Image Super-Resolution , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[13]  Pablo Navarrete Michelini,et al.  Multigrid Backprojection Super-Resolution and Deep Filter Visualization , 2019, AAAI.

[14]  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).

[15]  Luc Van Gool,et al.  NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

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

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

[18]  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.

[19]  Vladlen Koltun,et al.  Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.

[20]  Alexia Jolicoeur-Martineau,et al.  The relativistic discriminator: a key element missing from standard GAN , 2018, ICLR.

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

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

[23]  Radu Timofte,et al.  2018 PIRM Challenge on Perceptual Image Super-resolution , 2018, ArXiv.

[24]  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).

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

[26]  Luc Van Gool,et al.  NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[27]  Wan-Chi Siu,et al.  Hierarchical Back Projection Network for Image Super-Resolution , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[28]  Pablo Navarrete Michelini,et al.  Multi-scale Recursive and Perception-Distortion Controllable Image Super-Resolution , 2018, ECCV Workshops.

[29]  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).