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Sparsity Optimization in Design of Multidimensional Filter Networks
Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. (Medical Informatics)
Linköpings universitet, Matematiska institutionen, Optimeringslära. Linköpings universitet, Tekniska högskolan.ORCID-id: 0000-0003-1836-4200
Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.ORCID-id: 0000-0002-9091-4724
Linköpings universitet, Matematiska institutionen, Optimeringslära. Linköpings universitet, Tekniska högskolan. (Optimization)
2015 (engelsk)Inngår i: Optimization and Engineering, ISSN 1389-4420, E-ISSN 1573-2924, Vol. 16, nr 2, s. 259-277Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Filter networks are used as a powerful tool used for reducing the image processing time and maintaining high image quality.They are composed of sparse sub-filters whose high sparsity ensures fast image processing.The filter network design is related to solvinga sparse optimization problem where a cardinality constraint bounds above the sparsity level.In the case of sequentially connected sub-filters, which is the simplest network structure of those considered in this paper, a cardinality-constrained multilinear least-squares (MLLS) problem is to be solved. Even when disregarding the cardinality constraint, the MLLS is typically a large-scale problem characterized by a large number of local minimizers, each of which is singular and non-isolated.The cardinality constraint makes the problem even more difficult to solve.

An approach for approximately solving the cardinality-constrained MLLS problem is presented.It is then applied to solving a bi-criteria optimization problem in which both thetime and quality of image processing are optimized. The developed approach is extended to designing filter networks of a more general structure. Its efficiency is demonstrated by designing certain 2D and 3D filter networks. It is also compared with the existing approaches.

sted, utgiver, år, opplag, sider
Springer, 2015. Vol. 16, nr 2, s. 259-277
Emneord [en]
Sparse optimization; Cardinality Constraint; Multicriteria Optimization; Multilinear Least-Squares Problem; Filter networks; Medical imaging
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Identifikatorer
URN: urn:nbn:se:liu:diva-115788DOI: 10.1007/s11081-015-9280-3ISI: 000358253700001OAI: oai:DiVA.org:liu-115788DiVA, id: diva2:796663
Tilgjengelig fra: 2015-03-19 Laget: 2015-03-19 Sist oppdatert: 2017-12-04bibliografisk kontrollert

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Andersson, MatsBurdakov, OlegKnutsson, HansZikrin, Spartak

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