Open this publication in new window or tab >>2013 (English)Report (Other academic)
Abstract [en]
Filter networks is a powerful tool used for reducing the image processing time, while maintaining its reasonably high quality.They are composed of sparse sub-filters whose low 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. If to disregard the cardinality constraint, the MLLS is typically a large-scale problem characterized by a large number of local minimizers. Each of the local minimizers 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.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. p. 21
Series
LiTH-MAT-R, ISSN 0348-2960 ; 2013:16
Keywords
Sparse optimization; Cardinality Constraint; Multicriteria Optimization; Multilinear Least-Squares Problem; Filter networks; Medical imaging
National Category
Computational Mathematics Medical Image Processing Signal Processing
Identifiers
urn:nbn:se:liu:diva-103915 (URN)LiTH-MAT-R-2013/16-SE (ISRN)
2014-02-032014-02-032016-11-24Bibliographically approved