An Improved Total Variation-Based Image Fusion Algorithm

Abstract

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

CLC Number:

TP 391

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.

References

［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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