Convex optimization-based blind deconvolution for images taken with coherent illumination

Reinier Doelman, Michel Verhaegen

Research output: Contribution to journalArticleScientificpeer-review

70 Downloads (Pure)

Abstract

A rank-constrained reformulation of the blind deconvolution problem on images taken with coherent illumination is proposed. Since in the reformulation the rank constraint is imposed on a matrix that is affine in the decision variables, we propose a novel convex heuristic for the blind deconvolution problem. The proposed heuristic allows for easy incorporation of prior information on the decision variables and the use of the phase diversity concept. The convex optimization problem can be iteratively re-parameterized to obtain better estimates. The proposed methods are demonstrated on numerically illustrative examples.

Original languageEnglish
Pages (from-to)678-685
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Volume36
Issue number4
DOIs
Publication statusPublished - 2019

Fingerprint Dive into the research topics of 'Convex optimization-based blind deconvolution for images taken with coherent illumination'. Together they form a unique fingerprint.

  • Cite this