When does computational imaging improve performance?

Oliver Cossairt*, Mohit Gupta, Shree K. Nayar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

51 Scopus citations

Abstract

A number of computational imaging techniques are introduced to improve image quality by increasing light throughput. These techniques use optical coding to measure a stronger signal level. However, the performance of these techniques is limited by the decoding step, which amplifies noise. Although it is well understood that optical coding can increase performance at low light levels, little is known about the quantitative performance advantage of computational imaging in general settings. In this paper, we derive the performance bounds for various computational imaging techniques. We then discuss the implications of these bounds for several real-world scenarios (e.g., illumination conditions, scene properties, and sensor noise characteristics). Our results show that computational imaging techniques do not provide a significant performance advantage when imaging with illumination that is brighter than typical daylight. These results can be readily used by practitioners to design the most suitable imaging systems given the application at hand.

Original languageEnglish (US)
Article number6293888
Pages (from-to)447-458
Number of pages12
JournalIEEE Transactions on Image Processing
Volume22
Issue number2
DOIs
StatePublished - Jan 21 2013

Keywords

  • Computational imaging
  • computational photography
  • deconvolution
  • defocus deblurring
  • denoising
  • extended depth of field
  • image priors
  • image restoration
  • motion deblurring
  • multiplexing

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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