Unpaired Image Demoiréing Based on Cyclic Moiré Learning

Hyunkook Park, An Gia Vien, Yeong Jun Koh, Chul Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

We propose an end-to-end unsupervised learning approach to image demoiréing based on cyclic moiré learning. The proposed cyclic moiré learning consists of the moiré learning network and demoiréing network. The moiré learning network generates moiré images to construct a paired set of moiré and clean images. Then, the demoiréing network is trained using the generated paired dataset to remove moiré artifacts. Further, the moiré learning network and the demoiréing 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.

Original languageEnglish
Title of host publication2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages146-150
Number of pages5
ISBN (Electronic)9789881476890
StatePublished - 2021
Event2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Tokyo, Japan
Duration: 14 Dec 202117 Dec 2021

Publication series

Name2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings

Conference

Conference2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021
Country/TerritoryJapan
CityTokyo
Period14/12/2117/12/21

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