Abstract: In order to solve the problem that the fusion result is limited due to the over dependence on the label image when using the deep learning methods in the multi band image fusion field,a multi-band image feature-level self-supervised fusion method based on multi-discriminator generation adversarial network is proposed.Firstly,this paper designs and builds a feedback dense network as a feature enhancement module to separately extract multi-band image features and perform feature enhancement.Se-condly,it merges and connects the multi-band image feature enhanced results and reconstructs the fused image through the designed feature fusion module.Finally,the preliminary fused result and the source images of each band are input into the discriminator network respectively.Through the classification task of multiple discriminators,the generator is continuously optimized so that the output of the generator retains the characteristics of multiple band images at the same time to achieve the purpose of image fusion.Experimental results show that,compared with the current representative fusion method,the proposed method has better clarity,information volume,more detailed information,and is more in line with human visual characteristics.

Key words: Deep learning, Dense network, Generative adversarial network, Image fusion, Multi-band image, Self-supervised learning