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计算机工程 ›› 2011, Vol. 37 ›› Issue (13): 210-212. doi: 10.3969/j.issn.1000-3428.2011.13.068

• 图形图像处理 • 上一篇    下一篇

基于PCA分解的图像融合框架

潘 瑜,孙权森,夏德深   

  1. (南京理工大学计算机科学与技术学院,南京 210094)
  • 收稿日期:2010-12-30 出版日期:2011-07-05 发布日期:2011-07-05
  • 作者简介:潘 瑜(1984-),女,博士研究生,主研方向:模式识别,图像处理,图像融合;孙权森、夏德深,教授
  • 基金资助:
    国家自然科学基金资助项目(60773172)

Image Fusion Framework Based on PCA Decomposition

PAN Yu, SUN Quan-sen, XIA De-shen   

  1. (School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China)
  • Received:2010-12-30 Online:2011-07-05 Published:2011-07-05

摘要: 提出一种基于主成分分析(PCA)分解的图像融合框架。对源图像进行主成分分析,依据前几个主成分重建图像,经过下采样过程得到近似图像,对近似图像进行上采样,得到上层图像的差异图像(即细节图像),将最底层近似图像与各层细节图像进行累加完成图像的重构。实验结果表明,该方法能保持图像细节,具有较好的融合效果。

关键词: 图像融合, 主成分分析, 近似图像, 细节图像, 多尺度分解

Abstract: This paper proposes an image fusion framework based on Principal Component Analysis(PCA) decomposition. PCA is applied to the source images; then approximate images are acquired after subsampling the image reconstructed with the first few components; the differences between current approximate image and the last approximate image construct the detail image; the last approximate image and all of the detail images are merged into a new fused image. Experimental results demonstrate that this framework is able to get good performances in image fusion by keeping more details.

Key words: image fusion, Principal Component Analysis(PCA), approximate image, detail image, multi-scale decomposition

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