Matching Pursuit Based Convolutional Sparse Coding

Elad Plaut, Raja Giryes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

Convolutional sparse coding using the l0,∞ norm has been described as 'a problem that operates locally while thinking globally'. In this paper, we present a matching pursuit based greedy algorithm specifically tailored to the l0,∞ norm. We also propose a corresponding dictionary learning algorithm, which trains a local dictionary on a set of global images. Our approach is based on the convolutional relationship between the local dictionary and the global image. It operates locally while taking into account the global nature of the images. We demonstrate the usage of our proposed strategy for the task of image inpainting.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6847-6851
Number of pages5
ISBN (Print)9781538646588
DOIs
StatePublished - 10 Sep 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: 15 Apr 201820 Apr 2018

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2018-April
ISSN (Print)1520-6149

Conference

Conference2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Country/TerritoryCanada
CityCalgary
Period15/04/1820/04/18

Funding

FundersFunder number
ERC-StG

    Keywords

    • Convolutional Sparse Coding
    • Global modeling
    • Greedy Algorithms
    • Local Processing
    • Sparse Representations

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