| Convergence of the Schulz-Snyder phase retrieval algorithm to local minima. | |
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MedLine Citation:
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PMID: 16835639 Owner: NLM Status: PubMed-not-MEDLINE |
Abstract/OtherAbstract:
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The Schulz-Snyder iterative algorithm for phase retrieval attempts to recover a nonnegative function from its autocorrelation by minimizing the I-divergence between a measured autocorrelation and the autocorrelation of the estimated image. We illustrate that the Schulz-Snyder algorithm can become trapped in a local minimum of the I-divergence surface. To show that the estimates found are indeed local minima, sufficient conditions involving the gradient and the Hessian matrix of the I-divergence are given. Then we build a brief proof showing how an estimate that satisfies these conditions is a local minimum. The conditions are used to perform numerical tests determining local minimality of estimates. Along with the tests, related numerical issues are examined, and some interesting phenomena are discussed. |
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Authors:
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Kerkil Choi; Aaron D Lanterman; Raviv Raich |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Journal of the Optical Society of America. A, Optics, image science, and vision Volume: 23 ISSN: 1084-7529 ISO Abbreviation: J Opt Soc Am A Opt Image Sci Vis Publication Date: 2006 Aug |
Date Detail:
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Created Date: 2006-07-12 Completed Date: 2006-10-05 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9800943 Medline TA: J Opt Soc Am A Opt Image Sci Vis Country: United States |
Other Details:
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Languages: eng Pagination: 1835-45 Citation Subset: - |
Affiliation:
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School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. kerkil@untu.edu |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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