Robust removal of fixed pattern noise on multi-focus images

Kazuya Kodama, Kenta Fukui, Takayuki Hamamoto

研究成果: Conference contribution査読

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1318-1322
ページ数5
ISBN(電子版)9781509041176
DOI
出版ステータスPublished - 16 6月 2017
イベント2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
継続期間: 5 3月 20179 3月 2017

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(印刷版)1520-6149

Conference

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

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