Affiliations: [a] School of Software, Yunnan University, Kunming, Yunnan, China
| [b] Engineering Research Center of Cyberspace, Yunnan University, Kunming, China
| [c] School of Physics and Electronic Science, Normal University, Zunyi, China
| [d] School of Mathematics and Statistics, Yunnan University, Kunming, Yunnan, China
| [e] Institute of Technology Management, National Chiao Tung University, Hsinchu, Taiwan
Abstract: Multi-focus image fusion is a technique that integrates the focused areas in a pair or set of source images with the same scene into a fully focused image. Inspired by transfer learning, this paper proposes a novel color multi-focus image fusion method based on deep learning. First, color multi-focus source images are fed into VGG-19 network, and the parameters of convolutional layer of the VGG-19 network are then migrated to a neural network containing multilayer convolutional layers and multilayer skip-connection structures for feature extraction. Second, the initial decision maps are generated using the reconstructed feature maps of a deconvolution module. Third, the initial decision maps are refined and processed to obtain the second decision maps, and then the source images are fused to obtain the initial fused images based on the second decision maps. Finally, the final fused image is produced by comparing the QABF metrics of the initial fused images. The experimental results show that the proposed method can effectively improve the segmentation performance of the focused and unfocused areas in the source images, and the generated fused images are superior in both subjective and objective metrics compared with most contrast methods.
Keywords: Deep learning, feature extraction, multi-focus images fusion, neural networks, transfer learning
