摘要
图像融合的要求是尽可能多地融合源图像中的有用信息,并尽量不要把无用信息融合进来。为了尽可能达到这一要求,在分析盲源分离理论的基础上,提出了一种基于独立分量分析(ICA)的图像融合算法。该算法首先对源图像进行预处理;然后对源图像进行ICA分解,并在ICA域对独立分量系数进行融合;最后根据融合系数重建融合图像。实验结果表明,该新提出的算法降低了产生冗余信息的可能性,对多源图像融合是可行有效的。
Image fusion technology should try to achieve two objectives： one is to combine useful information of source images as much as possible; the other is to abandon the useless information of source images. Based on the analysis of blind signal separation theory, an ICA-based image fusion algorithm is proposed in this paper. Firstly, some pre-processing methods and ICA analysis are applied to each source image; then the ICA coefficients computed in the first step are fused using the maximum rule. Finally the fused image is obtained by synthesizing the fused ICA coefficients. Experiments prove that the new algorithm is feasible and effective for multi-source images fusion.
出处
《中国图象图形学报》
CSCD
北大核心
2007年第10期1857-1860,共4页
Journal of Image and Graphics
基金
国家自然科学基金项目(60673092)
教育部科研重点项目(205059)
江苏省高校自然科学基金项目(07KJD520186)
关键词
多源图像
图像融合
独立分量分析
矩阵稀疏性
multi-source image, image fusion, independent component analysis（ICA）, matrix sparsity
