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AIM 2019 Challenge on Image Demoireing: Methods and Results

8 November 2019
Shanxin Yuan
Radu Timofte
Greg Slabaugh
A. Leonardis
Bolun Zheng
Xin Ye
Xiang Tian
Yao-wu Chen
Xi Cheng
Zhenyong Fu
Jian Yang
Ming Hong
Wenying Lin
Wenjin Yang
Yanyun Qu
Hong-Kyu Shin
Joon-Yeon Kim
Sung-Jea Ko
Hang Dong
Yu Guo
Jie Wang
X. Ding
Zongyan Han
Sourya Dipta Das
Kuldeep Purohit
Praveen Kandula
Maitreya Suin
A. N. Rajagopalan
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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.

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