图像融合
人工智能
小波变换
计算机视觉
小波
模式识别(心理学)
第二代小波变换
计算机科学
融合
离散小波变换
图像(数学)
融合规则
数学
语言学
哲学
作者
Yuelin Zou,Xiaoqiang Liang,Tongming Wang
出处
期刊:Telkomnika: Indonesian Journal of Electrical Engineering
[Institute of Advanced Engineering and Science]
日期:2013-07-04
卷期号:11 (11)
被引量:28
标识
DOI:10.11591/telkomnika.v11i11.2898
摘要
In recent years image fusion plays a vital role in the image processing area. Fused images would help in doing many applications in image processing like segmentation, image enhancement and many.In order to improve the effect of fusion visible and infrared image images of the same scene, this paper presents an image fusion method based on lifting wavelet domain. Firstly, the source images are decomposed using lifting wavelet domain transform (LWT). Secondly, a weighted average approach based on normalized Shannon entropy is used to fuse low frequency lifting wavelet coefficients of the visible and infrared images. The fusion rule of local energy maximum is used to combine corresponding high frequency coefficients. After fusing low and high frequency coefficients of the source images, the final fused image is obtained using the inverse LWT. The experiments show that the proposed method provides improved subjective and objectives results as compared to previous image fusion methods such as Laplacian transform and traditional Wavelet transform. DOI: http://dx.doi.org/10.11591/telkomnika.v11i11.2898 Full Text: PDF
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