Unpaired Screen-Shot Image Demoiréing with Cyclic Moiré Learning

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

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

We propose an end-To-end unpaired learning approach to screen-shot image demoiréing based on cyclic moiré learning. The proposed cyclic moiré learning algorithm consists of the moiréing network and the demoiréing network. The moiréing network generates moiré images to construct a pseudo-paired set of moiré and clean images. Then, the demoiréing network is trained in a supervised manner using the generated pseudo-paired dataset to remove moiré artifacts. In the moiréing network, the moiré generation is separately learned as global pixel intensity degradation and moiré pattern generation for more realistic moiré artifact generation. Furthermore, the moiréing network and the demoiréing network are integrated together to be trained in an end-To-end manner. Experimental results on different datasets demonstrate that the proposed algorithm significantly outperforms state-of-The-Art unsupervised demoiréing algorithms as well as image restoration algorithms.

Original languageEnglish
Pages (from-to)16254-16268
Number of pages15
JournalIEEE Access
Volume10
DOIs
StatePublished - 2022

Keywords

  • cyclic moiré learning
  • Image demoiréing
  • intensity degradation
  • moiré pattern generation
  • unpaired learning

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