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

• Published in: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) pp. 3575 - 3583
• Main Authors: Lugmayr, Andreas, Danelljan, Martin, Timofte, Radu, Fritsche, Manuel, Gu, Shuhang, Purohit, Kuldeep, Kandula, Praveen, Suin, Maitreya, A. N., Rajagoapalan, Joon, Nam Hyung, Won, Yu Seung, Kim, Guisik, Kwon, Dokyeong, Hsu, Chih-Chung, Lin, Chia-Hsiang, Huang, Yuanfei, Sun, Xiaopeng, Lu, Wen, Li, Jie, Gao, Xinbo, Bell-Kligler, Sefi, Shocher, Assaf, Irani, Michal
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2019
• Subjects: Artificial neural networks; Benchmark testing; Deep Learning; Degradation; Image resolution; Real World; Super Resolution; Target tracking; Task analysis; Training
• ISSN: 2473-9944
• DOI: 10.1109/ICCVW.2019.00442

This paper reviews the AIM 2019 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided in the challenge. In Track 1: Source Domain the aim is to super-resolve such images while preserving the low level image characteristics of the source input domain. In Track 2: Target Domain a set of high-quality images is also provided for training, that defines the output domain and desired quality of the super-resolved images. To allow for quantitative evaluation, the source input images in both tracks are constructed using artificial, but realistic, image degradations. The challenge is the first of its kind, aiming to advance the state-of-the-art and provide a standard benchmark for this newly emerging task. In total 7 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem.