Generating Fluttering Patterns with Low Autocorrelation for Coded Exposure Imaging

Hae Gon Jeon, Joon Young Lee, Yudeog Han, Seon Joo Kim, In So Kweon

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

The performance of coded exposure imaging critically depends on finding good binary sequences. Previous coded exposure imaging methods have mostly relied on random searching to find the binary codes, but that approach can easily fail to find good long sequences, due to the exponentially expanding search space. In this paper, we present two algorithms for generating the binary sequences, which are especially well suited for generating short and long binary sequences, respectively. We show that the concept of low autocorrelation binary sequences, which has been successfully exploited in the field of information theory, can be applied to generate shutter fluttering patterns. We also propose a new measure for good binary sequences. Based on the new measure, we introduce two new algorithms for coded exposure imaging - a modified Legendre sequence method and a memetic algorithm. Experiments using both synthetic and real data show that our new algorithms consistently generate better binary sequences for the coded exposure problem, yielding better deblurring and resolution enhancement results compared to previous methods of generating the binary codes.

Original languageEnglish
Pages (from-to)269-286
Number of pages18
JournalInternational Journal of Computer Vision
Volume123
Issue number2
DOIs
Publication statusPublished - 2017 Jun 1

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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