Multi-focus image fusion based on fully convolutional networks

Abstract

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.

Keywords

Multi-focus image fusion / Fully convolutional networks / Skip layer / Performance evaluation

RIGHTS & PERMISSIONS

2020 Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2020