Convolutional Simultaneous Sparse Approximation with Applications to RGB-NIR Image Fusion

Farshad G. Veshki, Sergiy A. Vorobyov

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

1 Citation (Scopus)
17 Downloads (Pure)

Abstract

Simultaneous sparse approximation (SSA) seeks to represent a set of dependent signals using sparse vectors with identical supports. The SSA model has been used in various signal and image processing applications involving multiple correlated input signals. In this paper, we propose algorithms for convolutional SSA (CSSA) based on the alternating direction method of multipliers. Specifically, we address the CSSA problem with different sparsity structures and the convolutional feature learning problem in multimodal data/signals based on the SSA model. We evaluate the proposed algorithms by applying them to multimodal and multifocus image fusion problems.

Original languageEnglish
Title of host publication56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
EditorsMichael B. Matthews
PublisherIEEE
Pages872-876
Number of pages5
ISBN (Electronic)978-1-6654-5906-8
DOIs
Publication statusPublished - 7 Mar 2023
MoE publication typeA4 Conference publication
EventAsilomar Conference on Signals, Systems, and Computers - Virtual, Online, United States
Duration: 31 Oct 20222 Nov 2022

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2022-October
ISSN (Print)1058-6393

Conference

ConferenceAsilomar Conference on Signals, Systems, and Computers
Abbreviated titleACSSC
Country/TerritoryUnited States
CityVirtual, Online
Period31/10/202202/11/2022

Keywords

  • convolutional sparse coding
  • dictionary learning
  • image fusion
  • Simultaneous sparse approximation

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