A nonnegatively constrained convex programming method for image reconstruction

Johnathan M. Bardsley, Curtis R. Vogel

Research output: Contribution to journalArticlepeer-review

64 Scopus citations

Abstract

We consider a large-scale convex minimization problem with nonnegativity constraints that arises in astronomical imaging. We develop a cost functional which incorporates the statistics of the noise in the image data and Tikhonov regularization to induce stability. We introduce an efficient hybrid gradient projection-reduced Newton (active set) method. By "reduced Newton" we mean taking Newton steps only in the inactive variables. Due to the large size of our problem, we compute approximate reduced Newton steps using conjugate gradient (CG) iteration. We also introduce a highly effective sparse preconditioner that dramatically speeds up CG convergence. A numerical comparison between our method and other standard large-scale constrained minimization algorithms is presented.

Original languageEnglish
Pages (from-to)1326-1343
Number of pages18
JournalSIAM Journal on Scientific Computing
Volume25
Issue number4
DOIs
StatePublished - 2003

Keywords

  • Astronomical imaging
  • Constrained optimization

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