@inproceedings{eca5e23ca28e418284c35f9a50835f1a,
title = "Unpaired Image Demoir{\'e}ing Based on Cyclic Moir{\'e} Learning",
abstract = "We propose an end-to-end unsupervised learning approach to image demoir{\'e}ing based on cyclic moir{\'e} learning. The proposed cyclic moir{\'e} learning consists of the moir{\'e} learning network and demoir{\'e}ing network. The moir{\'e} learning network generates moir{\'e} images to construct a paired set of moir{\'e} and clean images. Then, the demoir{\'e}ing network is trained using the generated paired dataset to remove moir{\'e} artifacts. Further, the moir{\'e} learning network and the demoir{\'e}ing network are integrated together to be trained in an end-to-end manner. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art unsupervised image restoration al-gorithms.",
author = "Hyunkook Park and Vien, {An Gia} and Koh, {Yeong Jun} and Chul Lee",
note = "Publisher Copyright: {\textcopyright} 2021 APSIPA.; 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 ; Conference date: 14-12-2021 Through 17-12-2021",
year = "2021",
language = "English",
series = "2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "146--150",
booktitle = "2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings",
address = "United States",
}