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

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

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
EditorsMartial Hebert, Vittorio Ferrari, Cristian Sminchisescu, Yair Weiss
PublisherSpringer Verlag
Pages649-663
Number of pages15
ISBN (Print)9783030012489
DOIs
Publication statusPublished - Jan 1 2018
Event15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany
Duration: Sep 8 2018Sep 14 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11214 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th European Conference on Computer Vision, ECCV 2018
CountryGermany
CityMunich
Period9/8/189/14/18

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Yoshida, M., Torii, A., Okutomi, M., Endo, K., Sugiyama, Y., Taniguchi, R. I., & Nagahara, H. (2018). Joint optimization for compressive video sensing and reconstruction under hardware constraints. In M. Hebert, V. Ferrari, C. Sminchisescu, & Y. Weiss (Eds.), Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings (pp. 649-663). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11214 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-01249-6_39