Multi-focus image fusion with random walks and guided filters
- 179 Downloads
Multi-focus image fusion technique is able to help obtaining an all-focused image, which is advantage to human vision and image processing. In this paper, a novel multi-focus image fusion method is proposed based on random walk and guided filter. In the proposed method, the decomposition function and the optimizing function of random walk are used in multi-focus image fusion. And the random walk is also utilized for weight maps directly. The advantages of random walk and guided filter in image fusion are fully utilized by regulating proportional coefficients artificially. The proposed method concludes six steps: first, decomposing source images into detail layers and base layers with random walk; second, the random walk is used for weight maps directly and the guided filter is used as smoothing filters to get the streamlined weight maps of the detail layers and the base layers, respectively; third, the weight maps of the detail layers and the base layers are acquired by summing the initializing weight maps in different proportions; and then, the final weight maps of the detail layers are acquired using random walk for optimizing. After that, the fused detail layer and base layer are obtained by weighted average of detail layers and base layers, singly. Finally, the fused image is gained by summing up the fused base layer and the fused detail layer. Experiments demonstrate that the proposed method outperforms many other approaches in both subjective and objective assessments.
KeywordsImage fusion Multi-focus image Random walk Guided filter Weighted averaging
We would like to thank the associate editors and the reviewers for their valuable comments and suggestions. The authors also thank Shuai Wang for his generous help.
This study was funded by National Natural Science Foundation of China (Grant no. 61201421) and the Fundamental Research Funds for the Central Universities of Lanzhou University (lzujbky-2017-187).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- 3.Yan, C., et al.: Effective Uyghur language text detection in complex background images for traffic prompt identification. IEEE Trans. Intell. Transp. Syst. PP(99), 1–10 (2017)Google Scholar
- 11.Li, S., Kang, X., Hu, J.: Image fusion with guided filtering. IEEE Trans. Image Process. A Publ. IEEE Signal Process. Soc. 22(7), 2864 (2013)Google Scholar
- 15.Wang, Z., Wang, S., Guo, L.: Novel multi-focus image fusion based on PCNN and random walks. Neural Comput. Appl. 5, 1–14 (2016)Google Scholar
- 18.Tian, J., Chen, L.: Multi-focus image fusion using wavelet-domain statistics. In: IEEE International Conference on Image Processing (2010)Google Scholar
- 22.Yang, Y., et al.: Multifocus image fusion based on NSCT and focused area detection. IEEE Sens. J. 15(5), 2824–2838 (2015)Google Scholar
- 33.Burt, P.J.: A gradient pyramid basis for pattern-selective image fusion. In: Proceedings of the Society for Information Display Conference (1992)Google Scholar
- 34.Anderson, C.H.: Filter-subtract-decimate hierarchical pyramid signal analyzing and synthesizing technique (1988)Google Scholar
- 36.Wang, Z., et al.: Review of random walk in image processing. Arch. Comput. Methods Eng. 1866, 1–18 (2017)Google Scholar
- 37.Smolka, B., Wojciechowski, K.W., Szczepanski, M.: Random Walk Approach to Image Enhancement. In: Proceedings of International Conference on Image Analysis and Processing, 2001 (1999)Google Scholar
- 38.Ram, S., Rodríguez, J.J.: Random walker watersheds: a new image segmentation approach. In: IEEE International Conference on Acoustics, Speech and Signal Processing, (2013)Google Scholar
- 40.Grady, L., Funkalea, G.: Multi-label image segmentation for medical applications based on graph-theoretic electrical potentials. In: Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis, ECCV 2004 Workshops CVAMIA and MMBIA, Prague, Czech Republic, May 15, 2004, Revised Selected Papers (2004)Google Scholar
- 43.He, K., Sun, J., Tang, X.: Guided Image Filtering, pp. 1397–1409. Springer, Berlin (2010)Google Scholar
- 46.Wang, Z., et al.: An Image enhancement method based on edge preserving random walk filter. In: International Conference on Intelligent Computing (2015)Google Scholar
- 47.Wang, Z., Wang, H.: Image Smoothing with Generalized Random Walks, pp. 792–804. Elsevier Science Publishers B. V., Amsterdam (2016)Google Scholar
- 50.Qiang, W., Yi, S., Jing, J.: 19—Performance evaluation of image fusion techniques. In: Image fusion: algorithms and applications, pp. 469–492 (2008)Google Scholar
- 51.Xydeas, C.S., Petrovic, V.: Objective image fusion performance measure. Mil. Tech. Cour. 56(2), 181–193 (2000)Google Scholar