Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)

Multi-focus Image Fusion Algorithm Based on Multilevel Morphological Component Analysis

Authors
Lingling Wang, Xiongfei Li
Corresponding Author
Lingling Wang
Available Online March 2017.
DOI
https://doi.org/10.2991/ifmca-16.2017.40How to use a DOI?
Keywords
multilevel morphological component analysis; multi-focus image fusion; feature vectors; weighted fusion rules.
Abstract

This study proposed a novel image fusion algorithm based on weighed multilevel morphological component analysis (FWMMCA). First, morphological component analysis is improved into a new multi-scale decomposition algorithm–multilevel morphological component analysis (MMCA). Then, feature vectors are extracted from MMCA sub images, which are used to reflect brightness, texture regularity, energy, and randomness of those images. Moreover, the feature vectors are innovatively used as weight in fusion rules, to propose a weighted fusion rules. Sub images are fused via those fusion rules, which are finally reconstituted into fused image.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)
Series
Advances in Engineering Research
Publication Date
March 2017
ISBN
978-94-6252-307-4
ISSN
2352-5401
DOI
https://doi.org/10.2991/ifmca-16.2017.40How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Lingling Wang
AU  - Xiongfei Li
PY  - 2017/03
DA  - 2017/03
TI  - Multi-focus Image Fusion Algorithm Based on Multilevel Morphological Component Analysis
BT  - Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)
PB  - Atlantis Press
SP  - 248
EP  - 252
SN  - 2352-5401
UR  - https://doi.org/10.2991/ifmca-16.2017.40
DO  - https://doi.org/10.2991/ifmca-16.2017.40
ID  - Wang2017/03
ER  -