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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 603195, 14 pages
Research Article

Image Fusion with Contrast Improving and Feature Preserving

1Department of Computer Science and Information Engineering, National Central University, No. 300 Jhongda Road, Jhongli 32001, Taiwan
2Department of Electronic Engineering, Chien Hsin University of Science and Technology, No. 229 Jianxing Road, Jhongli 32097, Taiwan

Received 11 June 2014; Accepted 28 July 2014

Academic Editor: Teen-Hang Meen

Copyright © 2015 Din-Chang Tseng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The goal of image fusion is to obtain a fused image that contains most significant information in all input images which were captured by different sensors from the same scene. In particular, the fusion process should improve the contrast and keep the integrity of significant features from input images. In this paper, we propose a region-based image fusion method to fuse spatially registered visible and infrared images while improving the contrast and preserving the significant features of input images. At first, the proposed method decomposes input images into base layers and detail layers using a bilateral filter. Then the base layers of the input images are segmented into regions. Third, a region-based decision map is proposed to represent the importance of every region. The decision map is obtained by calculating the weights of regions according to the gray-level difference between each region and its neighboring regions in the base layers. At last, the detail layers and the base layers are separately fused by different fusion rules based on the same decision map to generate a final fused image. Experimental results qualitatively and quantitatively demonstrate that the proposed method can improve the contrast of fused images and preserve more features of input images than several previous image fusion methods.