Deep Learning-based Moiré Pattern Removal from a Single Image: A Survey and Comparative Study

Li Wei Kang, Chen Lo, Chia Hung Yeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Moiré pattern in a single image is common in image visual quality degradation induced by frequency aliasing between cameras and monitors when taking a screen-shot photo. It is mainly resulted from the interference between the pixel grids of the camera sensor and the device screen. However, removal of the Moiré patterns is challenging based on the complex frequency distribution and imbalanced magnitude in color channels. Only a few studies in the literature focused on the solution of Moiré pattern removal from a single image. Traditional studies usually treated the problem as an image denoising problem and applied some filtering or signal decomposition operations based on some image priors (e.g., sparsity). Relying on the rapid development of the deep learning techniques, some deep learning-based approaches of Moiré pattern removal have been presented recently. This paper presents a brief survey and comparative study for the recent deep learning-based research works on Moiré pattern removal and discusses possible further research directions.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728173993
DOIs
Publication statusPublished - 2020 Sep 28
Event7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 - Taoyuan, Taiwan
Duration: 2020 Sep 282020 Sep 30

Publication series

Name2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

Conference

Conference7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
Country/TerritoryTaiwan
CityTaoyuan
Period2020/09/282020/09/30

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Artificial Intelligence
  • Computer Science Applications
  • Signal Processing
  • Electrical and Electronic Engineering
  • Instrumentation

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