AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
By Pengxu Wei, Hannan Lu, Radu Timofte, Liang Lin, Wangmeng Zuo, Zhihong Pan, Baopu Li, Teng Xi, Yanwen Fan, Gang Zhang and 61 more. 2020
43 citations recorded by the source corpus.
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This challenge involves three tracks to super-resolve an input image for $\times$2, $\times$3 and $\times$4 scaling factors, respectively. The goal is to attract more attention to realistic image degradation for the SR task, which is much more complicated and challenging, and contributes to real-world image super-resolution applications. 452 participants were registered for three tracks in total, and 24 teams submitted their results. They gauge the state-of-the-art approaches for real image SR in terms of PSNR and SSIM.
Subfields: Computer Vision and Pattern Recognition, Media Technology, Evaluation & Benchmarks
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Citations in the corpus
- Designing a Practical Degradation Model for Deep Blind Image Super-Resolution (2021, methodology): “[46] separately established a SISR dataset with paired LR/HR camera images.”
- Real-World Single Image Super-Resolution: A Brief Review (2021, background): “Meanwhile, several challenges on RSISR have been organized in conjunction with the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision (ICCV), and European Conference on Computer Vision (ECCV) to attract more attention and promote the d”
- Efficient Image Super-Resolution Using Pixel Attention (2020, methodology): “To address this issue, the AIM 2020 [14,13,7,25,6,38,30] held the “Efficient Super Resolution” challenge [41], which asked the participants to use fewer computation cost”