TY - GEN
T1 - Moiré Pattern Removal with a Generative Adversarial Network
AU - Wang, Jinhui
AU - Chen, Lijiang
AU - Chen, Pengfei
AU - Mao, Xia
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/6/26
Y1 - 2020/6/26
N2 - Moiré patterns can be seen in camera-captured digital screen photos due to the interference between the pixel grids of the camera sensor and the pixel grids of the digital screen. It severely degrades the quality of the photos. With the rapid development of personal devices, people are using digital camera to take photos more and more often. Among them, it's very common to see camera-captured screen photos, so the work of Moiré pattern removal is very meaningful for improving user experience. In this paper, we introduce a novel method of Moiré pattern removal based on the Generative Adversarial Network (GAN). To train our model, we built a dataset of paired Ground and Moiré images, which has 16,500 images totally. Experiments showed that, given Moiré images as the input, the trained generator of our GAN nets can produce Moiré-free images of high quality.
AB - Moiré patterns can be seen in camera-captured digital screen photos due to the interference between the pixel grids of the camera sensor and the pixel grids of the digital screen. It severely degrades the quality of the photos. With the rapid development of personal devices, people are using digital camera to take photos more and more often. Among them, it's very common to see camera-captured screen photos, so the work of Moiré pattern removal is very meaningful for improving user experience. In this paper, we introduce a novel method of Moiré pattern removal based on the Generative Adversarial Network (GAN). To train our model, we built a dataset of paired Ground and Moiré images, which has 16,500 images totally. Experiments showed that, given Moiré images as the input, the trained generator of our GAN nets can produce Moiré-free images of high quality.
KW - Deep learning
KW - Generative Adversarial Network
KW - Moiré patterns
KW - Reconstruction
UR - https://www.scopus.com/pages/publications/85090396009
U2 - 10.1145/3406971.3406973
DO - 10.1145/3406971.3406973
M3 - 会议稿件
AN - SCOPUS:85090396009
T3 - ACM International Conference Proceeding Series
SP - 81
EP - 86
BT - ICGSP 2020 - Proceedings of the 4th International Conference on Graphics and Signal Processing
PB - Association for Computing Machinery
T2 - 4th International Conference on Graphics and Signal Processing, ICGSP 2020
Y2 - 26 June 2020 through 28 June 2020
ER -