• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Multi-focus Image Fusion using Fully Convolutional Two-stream Network for Visual Sensors


Abstract

We propose a deep learning method for multi-focus image fusion. Unlike most existing pixel-level fusion methods, either in spatial domain or in transform domain, our method directly learns an end-to-end fully convolutional two-stream network. The framework maps a pair of different focus images to a clean version, with a chain of convolutional layers, fusion layer and deconvolutional layers. Our deep fusion model has advantages of efficiency and robustness, yet demonstrates state-of-art fusion quality. We explore different parameter settings to achieve trade-offs between performance and speed. Moreover, the experiment results on our training dataset show that our network can achieve good performance with subjective visual perception and objective assessment metrics.


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Cite this article

[IEEE Style]
Kaiping Xu, Zheng Qin, Guolong Wang, Huidi Zhang, Kai Huang and Shuxiong Ye, "Multi-focus Image Fusion using Fully Convolutional Two-stream Network for Visual Sensors," KSII Transactions on Internet and Information Systems, vol. 12, no. 5, pp. 2253-2272, 2018. DOI: 10.3837/tiis.2018.05.019

[ACM Style]
Xu, K., Qin, Z., Wang, G., Zhang, H., Huang, K., and Ye, S. 2018. Multi-focus Image Fusion using Fully Convolutional Two-stream Network for Visual Sensors. KSII Transactions on Internet and Information Systems, 12, 5, (2018), 2253-2272. DOI: 10.3837/tiis.2018.05.019