Special Section on Quality Control by Artificial Vision

Multiscale image fusion using an adaptive similarity-based sensor weighting scheme and human visual system-inspired contrast measure

[+] Author Affiliations
Shahan C. Nercessian

Tufts University, Department of Electrical and Computer Engineering, Medford, Massachusetts 02155

Karen A. Panetta

Tufts University, Department of Electrical and Computer Engineering, Medford, Massachusetts 02155

Sos S. Agaian

The University of Texas at San Antonio, Department of Electrical and Computer Engineering, San Antonio, Texas 78249

J. Electron. Imaging. 21(2), 021112 (May 10, 2012). doi:10.1117/1.JEI.21.2.021112
History: Received September 22, 2011; Revised February 14, 2012; Accepted February 17, 2012
Text Size: A A A

Abstract.  The goal of image fusion is to combine multiple source images obtained using different capture techniques into a single image to provide an effective contextual enhancement of a scene for human or machine perception. In practice, considerable value can be gained in the fusion of images that are dissimilar or complementary in nature. However, in such cases, global weighting schemes may not sufficiently weigh the contribution of the pertinent information of the source images, while existing adaptive schemes calculate weights based on the relative amounts of salient features, which can cause severe artifacting or inadequate local luminance in the fusion result. Accordingly, a new multiscale image fusion algorithm is proposed. The approximation coefficient fusion rule of the algorithm is based on a novel similarity based weighting scheme capable of providing improved fusion results when the input source images are either similar or dissimilar to each other. Moreover, the algorithm employs a new detail coefficient fusion rule integrating a parametric multiscale contrast measure. The parametric nature of the contrast measure allows the degree to which psychophysical laws of human vision hold to be tuned based on image-dependent characteristics. Experimental results illustrate the superior performance of the proposed algorithm qualitatively and quantitatively.

Figures in this Article
© 2012 SPIE and IS&T

Citation

Shahan C. Nercessian ; Karen A. Panetta and Sos S. Agaian
"Multiscale image fusion using an adaptive similarity-based sensor weighting scheme and human visual system-inspired contrast measure", J. Electron. Imaging. 21(2), 021112 (May 10, 2012). ; http://dx.doi.org/10.1117/1.JEI.21.2.021112


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

PubMed Articles
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.