Composite SAR imaging using sequential joint sparsity

Toby Sanders, Anne Gelb, Rodrigo Platte

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


This paper investigates accurate and efficient ℓ1 regularization methods for generating synthetic aperture radar (SAR) images. Although ℓ1 regularization algorithms are already employed in SAR imaging, practical and efficient implementation in terms of real time imaging remain a challenge. Here we demonstrate that fast numerical operators can be used to robustly implement ℓ1 regularization methods that are as or more efficient than traditional approaches such as back projection, while providing superior image quality. In particular, we develop a sequential joint sparsity model for composite SAR imaging which naturally combines the joint sparsity methodology with composite SAR. Our technique, which can be implemented using standard, fractional, or higher order total variation regularization, is able to reduce the effects of speckle and other noisy artifacts with little additional computational cost. Finally we show that generalizing total variation regularization to non-integer and higher orders provides improved flexibility and robustness for SAR imaging.

Original languageEnglish (US)
Pages (from-to)357-370
Number of pages14
JournalJournal of Computational Physics
StatePublished - Jun 1 2017


  • Image reconstruction
  • Joint sparsity
  • Synthetic aperture radar
  • ℓ regularization

ASJC Scopus subject areas

  • Physics and Astronomy (miscellaneous)
  • Computer Science Applications


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