Robust removal of fixed pattern noise on multi-focus images

Kazuya Kodama, Kenta Fukui, Takayuki Hamamoto

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

1 Citation (Scopus)

Abstract

In this paper, we propose a novel method restoring multi-focus images based on convex optimization with new constraint for fixed pattern noise. Even weak fixed pattern noise on multi-focus images degrades all-in-focus images reconstructed by linear combination of them, especially, when using telecentric optical systems such as microscopes. Our novel method introduces constraint for additive fixed pattern noise into total variation minimization and then it is improved for multiplicative fixed pattern noise. The proposed method suppresses fixed pattern noise on multi-focus images very robustly to avoid such degradation on reconstructed images. Experimental results show that our method achieves high performance compared to simple total variation minimization.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1318-1322
Number of pages5
ISBN (Electronic)9781509041176
DOIs
Publication statusPublished - 16 Jun 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: 5 Mar 20179 Mar 2017

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Country/TerritoryUnited States
CityNew Orleans
Period5/03/179/03/17

Keywords

  • convex optimization
  • fixed pattern noise
  • focus
  • image reconstruction
  • image restoration

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