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Proceedings Paper

Multi-focus and multi-modal fusion: a study of multi-resolution transforms
Author(s): Michael Giansiracusa; Adam Lutz; Soundararajan Ezekiel; Mark Alford; Erik Blasch; Adnan Bubalo; Millicent Thomas
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Paper Abstract

Automated image fusion has a wide range of applications across a multitude of fields such as biomedical diagnostics, night vision, and target recognition. Automation in the field of image fusion is difficult because there are many types of imagery data that can be fused using different multi-resolution transforms. The different image fusion transforms provide coefficients for image fusion, creating a large number of possibilities. This paper seeks to understand how automation could be conceived for selected the multiresolution transform for different applications, starting in the multifocus and multi-modal image sub-domains. The study analyzes the greatest effectiveness for each sub-domain, as well as identifying one or two transforms that are most effective for image fusion. The transform techniques are compared comprehensively to find a correlation between the fusion input characteristics and the optimal transform. The assessment is completed through the use of no-reference image fusion metrics including those of information theory based, image feature based, and structural similarity based methods.

Paper Details

Date Published: 31 May 2016
PDF: 10 pages
Proc. SPIE 9841, Geospatial Informatics, Fusion, and Motion Video Analytics VI, 98410I (31 May 2016); doi: 10.1117/12.2224347
Show Author Affiliations
Michael Giansiracusa, Indiana Univ. of Pennsylvania (United States)
Adam Lutz, Indiana Univ. of Pennsylvania (United States)
Soundararajan Ezekiel, Indiana Univ. of Pennsylvania (United States)
Mark Alford, Air Force Research Lab. (United States)
Erik Blasch, Air Force Research Lab. (United States)
Adnan Bubalo, Air Force Research Lab. (United States)
Millicent Thomas, Northwest Univ. (United States)

Published in SPIE Proceedings Vol. 9841:
Geospatial Informatics, Fusion, and Motion Video Analytics VI
Matthew F. Pellechia; Kannappan Palaniappan; Peter J. Doucette; Shiloh L. Dockstader; Gunasekaran Seetharaman; Paul B. Deignan, Editor(s)

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