Fourier Phase Retrieval With Extended Support Estimation via Deep Neural Network

Cited 1 time in webofscience Cited 0 time in scopus
  • Hit : 140
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorKim, Kyung-Suko
dc.contributor.authorChung, Sae-Youngko
dc.date.accessioned2019-10-02T10:20:31Z-
dc.date.available2019-10-02T10:20:31Z-
dc.date.created2019-10-01-
dc.date.issued2019-10-
dc.identifier.citationIEEE SIGNAL PROCESSING LETTERS, v.26, no.10, pp.1506 - 1510-
dc.identifier.issn1070-9908-
dc.identifier.urihttp://hdl.handle.net/10203/267732-
dc.description.abstractWe consider the problem of sparse phase retrieval from Fourier transform magnitudes to recover the k-sparse signal vector and its support T. We exploit extended support estimate epsilon with size larger than k satisfying epsilon superset of T and obtained by a trained deep neural network (DNN). To make the DNN learnable, it provides epsilon as the union of equivalent solutions of T by utilizing modulo Fourier invariances. Set epsilon can be estimated with short running time via the DNN, and support T can he determined from the DNN output rather than from the full index set by applying hard thresholding to epsilon. Thus, the DNN-based extended support estimation improves the reconstruction performance of the signal with a low complexity burden dependent on k. Numerical results verify that the proposed scheme has a superior performance with lower complexity compared to local search-based greedy sparse phase retrieval and a state-of-the-art variant of the Fienup method.-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleFourier Phase Retrieval With Extended Support Estimation via Deep Neural Network-
dc.typeArticle-
dc.identifier.wosid000485740400004-
dc.identifier.scopusid2-s2.0-85072228898-
dc.type.rimsART-
dc.citation.volume26-
dc.citation.issue10-
dc.citation.beginningpage1506-
dc.citation.endingpage1510-
dc.citation.publicationnameIEEE SIGNAL PROCESSING LETTERS-
dc.identifier.doi10.1109/LSP.2019.2935814-
dc.contributor.localauthorChung, Sae-Young-
dc.contributor.nonIdAuthorKim, Kyung-Su-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorDeep neural network-
dc.subject.keywordAuthorextended support estimation-
dc.subject.keywordAuthorFourier transform-
dc.subject.keywordAuthorsparse phase retrieval-
Appears in Collection
EE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 1 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0