Optimal image-fusion method based on nonsubsampled contourlet transform

Jianfang Dou; Jianxun Li

doi:10.1117/1.OE.51.10.107006

12 October 2012 Optimal image-fusion method based on nonsubsampled contourlet transform

Jianfang Dou, Jianxun Li

Author Affiliations +

Optical Engineering, Vol. 51, Issue 10, 107006 (October 2012). https://doi.org/10.1117/1.OE.51.10.107006

Abstract

The optimization of image fusion is researched. Based on the properties of nonsubsampled contourlet transform (NSCT), shift invariance, multiscale and multidirectional expansion, the fusion parameters of the multiscale decompostion scheme is optimized. In order to meet the requirement of feedback optimization, a new image fusion quality metric of image quality index normalized edge association (IQI-NEA) is built. A polynomial model is adopted to establish the relationship between the IQI_NEA metric and several decomposition levels. The optimal fusion includes four steps. First, the source images are decomposed in NSCT domain for several given levels. Second, principal component analysis is adopted to fuse the low frequency coefficients and the maximum fusion rule is utilized to fuse the high frequency coefficients to obtain the fused coefficients and the fused result is reconstructed from the obtained fused coefficients. Third, calculate the fusion quality metric IQI_NEA for the source images and fused images. Finally, the optimal fused image and optimal level are obtained through extremum properties of polynomials function. The visual and statistical results show that the proposed method has optimized the fusion performance compared to the existing fusion schemes, in terms of the visual effects and quantitative fusion evaluation indexes.

Citation Download Citation

Jianfang Dou and Jianxun Li "Optimal image-fusion method based on nonsubsampled contourlet transform," Optical Engineering 51(10), 107006 (12 October 2012). https://doi.org/10.1117/1.OE.51.10.107006

Published: 12 October 2012

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available