Computational spectral and ultrafast imaging via convex optimization

Figen S. Oktem, Liang Gao, Farzad Kamalabadi

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Multidimensional optical imaging, that is, capturing light in more than two-dimensions (unlike conventional photography), has been an emerging field with widespread applications in diverse domains. Due to the intrinsic limitation of two-dimensional detectors in capturing inherently higher-dimensional data, multidimensional imaging techniques conventionally rely on a scanning process, which renders them inefficient in terms of light throughput and unsuitable for dynamic scenes. In this chapter, we present recent multidimensional imaging techniques for spectral and temporal imaging, which overcome the temporal, spectral, and spatial resolution limitations of conventional scanning-based systems. Each development is based on the computational imaging paradigm, which involves distributing the imaging task between a physical and a computational system and then digitally forming the image datacube of interest from multiplexed measurements by means of solving an inverse problem via convex optimization techniques.

Original languageEnglish (US)
Title of host publicationHandbook of Convex Optimization Methods in Imaging Science
PublisherSpringer International Publishing
Pages105-127
Number of pages23
ISBN (Electronic)9783319616094
ISBN (Print)9783319616087
DOIs
StatePublished - Jan 1 2017

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

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    Oktem, F. S., Gao, L., & Kamalabadi, F. (2017). Computational spectral and ultrafast imaging via convex optimization. In Handbook of Convex Optimization Methods in Imaging Science (pp. 105-127). Springer International Publishing. https://doi.org/10.1007/978-3-319-61609-4_5