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AIM 2019 challenge on image demoireing: Methods and results
Date Issued
01-10-2019
Author(s)
Yuan, Shanxin
Timofte, Radu
Slabaugh, Greg
Leonardis, Ales
Zheng, Bolun
Ye, Xin
Tian, Xiang
Chen, Yaowu
Cheng, Xi
Fu, Zhenyong
Yang, Jian
Hong, Ming
Lin, Wenying
Yang, Wenjin
Qu, Yanyun
Shin, Hong Kyu
Kim, Joon Yeon
Ko, Sung Jea
Dong, Hang
Guo, Yu
Wang, Jie
Ding, Xuan
Han, Zongyan
Das, Sourya DIpta
Purohit, Kuldeep
Kandula, Praveen
Suin, Maitreya
Rajagoapalan, A. N.
Abstract
This paper reviews the first-ever image demoireing challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ICCV 2019. This paper describes the challenge, and focuses on the proposed solutions and their results. Demoireing is a difficult task of removing moire patterns from an image to reveal an underlying clean image. A new dataset, called LCDMoire was created for this challenge, and consists of 10,200 synthetically generated image pairs (moire and clean ground truth). The challenge was divided into 2 tracks. Track 1 targeted fidelity, measuring the ability of demoire methods to obtain a moire-free image compared with the ground truth, while Track 2 examined the perceptual quality of demoire methods. The tracks had 60 and 39 registered participants, respectively. A total of eight teams competed in the final testing phase. The entries span the current the state-of-the-art in the image demoireing problem.