一种改进的基于全变差范数图像融合方法

上海交通大学学报（自然版） ›› 2013, Vol. 47 ›› Issue (04): 607-612.

• 自动化技术、计算机技术 • 上一篇下一篇

一种改进的基于全变差范数图像融合方法

郝珉慧, 刘哲, 张永亮, 张鹤妮

(西北工业大学理学院，西安 710129)

收稿日期:2012-05-07 出版日期:2013-04-28 发布日期:2013-04-28
基金资助:
国家自然科学基金 (61071170)资助项目，教育部新世纪优秀人才支持计划资助项目

An Improved Total Variation-Based Image Fusion Algorithm

HAO Min-Hui, LIU Zhe, ZHANG Yong-Liang, ZHANG He-Ni

(School of Science，Northwestern Polytechnical University， Xi’an 710129, China)

Received:2012-05-07 Online:2013-04-28 Published:2013-04-28

摘要/Abstract

摘要： 针对基于全变差范数图像融合方法在多聚焦图像融合中产生分块效应的缺陷，提出将区域的特征表示与主成分分析思想相结合的改进算法，解决了单独块变换造成的图像分块效应问题.实验结果表明，改进算法能有效地消除分块效应，且针对含噪图像，其融合效果明显优于离散小波方法.

关键词: 图像融合, 全变差范数, 多聚焦图像, 分块效应

Abstract: Considering the total variation-based algorithm for image fusion has block effects when fusing multi-focus images, the notion of feature representation in conjunction with principal component analysis was used for improvement. The improved approach prevents independent block transform from happening. The experimental results show that the proposed algorithm can resolve the problem of block effects efficiently, and it also outperforms the discrete wavelet transform based algorithm when focusing on noisy image. Key words：

Key words: image fusion, total variation norm, multi-focus images, block effect

中图分类号:

TP 391

郝珉慧, 刘哲, 张永亮, 张鹤妮. 一种改进的基于全变差范数图像融合方法[J]. 上海交通大学学报（自然版）, 2013, 47(04): 607-612.

HAO Min-Hui, LIU Zhe, ZHANG Yong-Liang, ZHANG He-Ni. An Improved Total Variation-Based Image Fusion Algorithm [J]. Journal of Shanghai Jiaotong University, 2013, 47(04): 607-612.

参考文献

［1］Dong Jiang, Zhuang Dafang, Huang Yaohuan, et al. Advances in multisensor data fusion: Algorithms and applications［J］. Sensors, 2009, 9(10): 77717784.
［2］徐显，杨杰. 基于图形处理器硬件加速的高精度医学图像融合算法［J］. 上海交通大学学报, 2010, 44(2): 248258.
XU Xian, YANG Jie. High degree of accuracy medical image fusion algorithm based on GPU hardware acceleration［J］. Journal of Shanghai Jiaotong University, 2010, 44(2): 248258.
［3］Chang Huawen, Lan Shuduo. Image fusion based on addition of wavelet coefficients［C］∥Proceedings of International Conference on Wavelet Analysis and Pattern Recognition. NewYork: IEEE, 2007: 15851588.
［4］Mrityunjay K, Sarat D. A total variationbased algorithm for pixellevel image fusion［J］. IEEE Transactions on Image Processing, 2009, 18(9): 21372143.
［5］Rudin L, Osher S, Fatemi E. Nonlinear totalvariation based noise removal algorithms［J］. Phys D, 1992, 60:259268.
［6］Chan T, Esedoglu S, Park F, et al. Recent development in total variation image restoration［C］∥Mathematical Models of Computer Vision. New York: Springer Verlag, 2005.
［7］Bonettini S, Ruggiero V. An alternating extragradient method for total variation based image restoration from Poisson data［J］. Inverse Problems, 2011, 27(9): 126.
［8］Liu Zheng, David S F, Robert L . A featurebased metric for the quantitative evaluation of pixellevel image fusion［J］. Computer Vision and Image Understanding, 2008, 109(11): 5668.
［9］Mumtaz Aqeel, Majid A, Mumtaz Adeel. Genetic algorithms and its application to image fusion［C］∥Proceedings of the 4th International Conference on Emerging Technologies. Rawalpindi, Pakistan: IEEE, 2008, 610.

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一种改进的基于全变差范数图像融合方法

An Improved Total Variation-Based Image Fusion Algorithm

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