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-FC...

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Bibliographic Details
Published in:	Frontiers of information technology & electronic engineering 2020-07, Vol.21 (7), p.1019-1033
Main Authors:	Guo, Rui, Shen, Xuan-jing, Dong, Xiao-yu, Zhang, Xiao-li
Format:	Article
Language:	eng
Subjects:	Algorithms Communications Engineering Computer Hardware Computer Science Computer Systems Organization and Communication Networks Computer vision Datasets Electrical Engineering Electronics and Microelectronics Image processing Instrumentation Methods Morphology Networks Neural networks
Online Access:	Get full text
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Summary:	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.
ISSN:	2095-9184 2095-9230