Joint optimization for compressive video sensing and reconstruction under hardware constraints

Michitaka Yoshida, Akihiko Torii, Masatoshi Okutomi, Kenta Endo, Yukinobu Sugiyama, Rin Ichiro Taniguchi, Hajime Nagahara

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

1 被引用数 (Scopus)

抄録

Compressive video sensing is the process of encoding multiple sub-frames into a single frame with controlled sensor exposures and reconstructing the sub-frames from the single compressed frame. It is known that spatially and temporally random exposures provide the most balanced compression in terms of signal recovery. However, sensors that achieve a fully random exposure on each pixel cannot be easily realized in practice because the circuit of the sensor becomes complicated and incompatible with the sensitivity and resolution. Therefore, it is necessary to design an exposure pattern by considering the constraints enforced by hardware. In this paper, we propose a method of jointly optimizing the exposure patterns of compressive sensing and the reconstruction framework under hardware constraints. By conducting a simulation and actual experiments, we demonstrated that the proposed framework can reconstruct multiple sub-frame images with higher quality.

本文言語英語
ホスト出版物のタイトルComputer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
編集者Martial Hebert, Vittorio Ferrari, Cristian Sminchisescu, Yair Weiss
出版社Springer Verlag
ページ649-663
ページ数15
ISBN(印刷版)9783030012489
DOI
出版ステータス出版済み - 2018
イベント15th European Conference on Computer Vision, ECCV 2018 - Munich, ドイツ
継続期間: 9月 8 20189月 14 2018

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11214 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他15th European Conference on Computer Vision, ECCV 2018
国/地域ドイツ
CityMunich
Period9/8/189/14/18

!!!All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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