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Convolutional group-sparse coding and source localization
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0003-3054-7210
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0001-6630-243X
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we present a new interpretation of non-negativelyconstrained convolutional coding problems as blind deconvolution problems with spatially variant point spread function. In this light, we propose an optimization framework that generalizes our previous work on non-negative group sparsity for convolutional models. We then link these concepts to source localization problems that arise in scientific imaging, and provide a visual example on an image derived from data captured by the Hubble telescope.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2018.
Keywords [en]
Sparse representation, Source localization, Non-negative group sparsity
National Category
Signal Processing
Research subject
Electrical Engineering; Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-224253OAI: oai:DiVA.org:kth-224253DiVA, id: diva2:1190635
Conference
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), April 15-20, Calgary, Alberta, Canada
Funder
Swedish Research Council, 2015-04026
Note

QCR 20180319

Available from: 2018-03-15 Created: 2018-03-15 Last updated: 2018-03-19Bibliographically approved

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del Aguila Pla, PolJaldén, Joakim

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