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Journal of Electronic Imaging

Joint image registration and fusion method with a gradient strength regularization
Author(s): Huang Lidong; Zhao Wei; Wang Jun
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Paper Abstract

Image registration is an essential process for image fusion, and fusion performance can be used to evaluate registration accuracy. We propose a maximum likelihood (ML) approach to joint image registration and fusion instead of treating them as two independent processes in the conventional way. To improve the visual quality of a fused image, a gradient strength (GS) regularization is introduced in the cost function of ML. The GS of the fused image is controllable by setting the target GS value in the regularization term. This is useful because a larger target GS brings a clearer fused image and a smaller target GS makes the fused image smoother and thus restrains noise. Hence, the subjective quality of the fused image can be improved whether the source images are polluted by noise or not. We can obtain the fused image and registration parameters successively by minimizing the cost function using an iterative optimization method. Experimental results show that our method is effective with transformation, rotation, and scale parameters in the range of [−2.0, 2.0] pixel, [−1.1  deg, 1.1 deg], and [0.95, 1.05], respectively, and variances of noise smaller than 300. It also demonstrated that our method yields a more visual pleasing fused image and higher registration accuracy compared with a state-of-the-art algorithm.

Paper Details

Date Published: 5 June 2015
PDF: 15 pages
J. Electron. Imaging. 24(3) 033018 doi: 10.1117/1.JEI.24.3.033018
Published in: Journal of Electronic Imaging Volume 24, Issue 3
Show Author Affiliations
Huang Lidong, BeiHang Univ. (China)
Zhao Wei, BeiHang Univ. (China)
Wang Jun, BeiHang Univ. (China)


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