| On the complexity of mumford-shah-type regularization, viewed as a relaxed sparsity constraint. | |
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MedLine Citation:
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PMID: 20421187 Owner: NLM Status: PubMed-not-MEDLINE |
Abstract/OtherAbstract:
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We show that inverse problems with a truncated quadratic regularization are NP-hard in general to solve or even approximate up to an additive error. This stands in contrast to the case corresponding to a finite-dimensional approximation to the Mumford-Shah functional, where the operator involved is the identity and for which polynomial-time solutions are known. Consequently, we confirm the infeasibility of any natural extension of the Mumford-Shah functional to general inverse problems. A connection between truncated quadratic minimization and sparsity-constrained minimization is also discussed. |
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Authors:
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Boris Alexeev; Rachel Ward |
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Publication Detail:
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Type: Letter; Research Support, U.S. Gov't, Non-P.H.S. Date: 2010-04-22 |
Journal Detail:
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Title: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Volume: 19 ISSN: 1941-0042 ISO Abbreviation: IEEE Trans Image Process Publication Date: 2010 Oct |
Date Detail:
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Created Date: 2010-09-20 Completed Date: 2011-01-10 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9886191 Medline TA: IEEE Trans Image Process Country: United States |
Other Details:
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Languages: eng Pagination: 2787-9 Citation Subset: - |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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