Multi-focus image fusion based on fully convolutional networks
Rui GUO , Xuan-jing SHEN , Xiao-yu DONG , Xiao-li ZHANG
Front. Inform. Technol. Electron. Eng ›› 2020, Vol. 21 ›› Issue (7) : 1019 -1033.
Multi-focus image fusion based on fully convolutional networks
We propose a multi-focus image fusion method, in which a fully convolutional network for focus detection (FD-FCN) is constructed. To obtain more precise focus detection maps, we propose to add skip layers in the network to make both detailed and abstract visual information available when using FD-FCN to generate maps. A new training dataset for the proposed network is constructed based on dataset CIFAR-10. The image fusion algorithm using FD-FCN contains three steps: focus maps are obtained using FD-FCN, decision map generation occurs by applying a morphological process on the focus maps, and image fusion occurs using a decision map. We carry out several sets of experiments, and both subjective and objective assessments demonstrate the superiority of the proposed fusion method to state-of-the-art algorithms.
Multi-focus image fusion / Fully convolutional networks / Skip layer / Performance evaluation
Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2020
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