Multi-Focus Image Fusion Based on Multi-Scale Generative Adversarial Network |
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| Authors: | Xiaole Ma Zhihai Wang Shaohai Hu Shichao Kan |
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| Affiliation: | 1.School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China; (X.M.); (Z.W.);2.Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China;3.School of Computer Science and Engineering, Central South University, Changsha 410083, China; |
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| Abstract: | The methods based on the convolutional neural network have demonstrated its powerful information integration ability in image fusion. However, most of the existing methods based on neural networks are only applied to a part of the fusion process. In this paper, an end-to-end multi-focus image fusion method based on a multi-scale generative adversarial network (MsGAN) is proposed that makes full use of image features by a combination of multi-scale decomposition with a convolutional neural network. Extensive qualitative and quantitative experiments on the synthetic and Lytro datasets demonstrated the effectiveness and superiority of the proposed MsGAN compared to the state-of-the-art multi-focus image fusion methods. |
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| Keywords: | multi-scale decomposition generative adversarial network multi-focus image fusion |
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