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Proceedings Paper

Image fusion based on wavelet transform and visual attention mechanism
Author(s): Xianyi Ren; Wei Liu; Peng Duan; Sanfeng Chen
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Paper Abstract

Image fusion is a process of combing multiple images of the same scene into a single image with the aim to preserve the full content information and retain the important features from each of the combined images. In this paper, a novel image fusion method based on Wavelet Transform (WT) and Visual Attention Mechanism (VAM) is proposed. Firstly, the source images are decomposed by WT to get the sub-images. Secondly, by using the VAM, the salience maps of the source images are formed, which can indicate the salient regions to the human visual system, i.e., the higher the saliency value is, the more important the location represent. The saliency maps are then used together with the match measure between the coefficients to guide the combination of the coefficients as follows: if the match measure at a given location is low, the coefficient from the source image with higher saliency is selected to be the fused coefficient. Contrarily, if the match measure at a given location is high, then the fused coefficients are calculated as the weighted sum of the coefficients extracted from both source images. The weights here are determined by the corresponding saliency maps of the source images. Finally, the fusion image is obtained by using the inverse WT transform. Experimental results applying the proposed algorithm show that the fused image keeps more visual meaningful information than other methods.

Paper Details

Date Published: 30 October 2009
PDF: 6 pages
Proc. SPIE 7494, MIPPR 2009: Multispectral Image Acquisition and Processing, 74941X (30 October 2009); doi: 10.1117/12.833235
Show Author Affiliations
Xianyi Ren, Shenzhen Institute of Information Technology (China)
Wei Liu, Shenzhen Institute of Information Technology (China)
Peng Duan, Shenzhen Institute of Information Technology (China)
Sanfeng Chen, Shenzhen Institute of Information Technology (China)


Published in SPIE Proceedings Vol. 7494:
MIPPR 2009: Multispectral Image Acquisition and Processing

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