ECA-based generative adversarial network for multi-focus colour image fusion

Abstract

In this paper, the idea of regression model is adopted to complete the fusion of multi-focus images through an end-to-end generative adversarial network (GAN). In the generator part, image features are extracted through multi-branch connection and dense connection technology. In the process of extracting high-dimensional image features, the ECA module is embedded to improve the capability of network. In the discriminator part, the idea of relative GAN is used to predict the relative authenticity between images. Due to the idea and reasonable network construction, the method proposed in this paper can obtain good results of image fusion. And the experimental results demonstrate that the one can also obtain fine results in objective evaluation, which is better than the compared algorithms.