Singular-value decomposition for through-focus imaging systems

Anna Burvall, Harrison H. Barrett, Christopher Dainty, Kyle J. Myers

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Singular-value decomposition (SVD) of a linear imaging system gives information on the null and measurement components of object and image and provides a method for object reconstruction from image data. We apply SVD to through-focus imaging systems that produce several two-dimensional images of a three-dimensional object. Analytical expressions for the singular functions are derived in the geometrical approximation for a telecentric, laterally shift-invariant system linear in intensity. The modes are evaluated numerically, and their accuracy confirmed. Similarly, the modes are derived and evaluated for a continuous image representing the limit of a large number of image planes.

Original languageEnglish (US)
Pages (from-to)2440-2448
Number of pages9
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Issue number10
StatePublished - Oct 2006

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Computer Vision and Pattern Recognition


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