MGFA : A multi-scale global feature autoencoder to fuse infrared and visible images

Since the convolutional operation pays too much attention to local information, resulting in the loss of global information and a decline in fusion quality. In order to ensure that the fused image fully captures the features of the entire scene, an end-to-end Multi-scale Global Feature Autoencoder (MGFA) is proposed in this paper, which can generate fused images with both global and local information. In this network, a multi-scale global feature extraction module is proposed, which combines dilated convolutional modules with the Global Context Block (GCBlock) to extract the global features ignored by the convolutional operation. In addition, an adaptive embedded residual fusion module is proposed to fuse different frequency components in the source images with the idea of embedded residual learning. This can enrich the detailed texture of the fused results. Extensive qualitative and quantitative experiments have demonstrated that the proposed method can achieve excellent results in retaining global information and improving visual effects. Furthermore, the fused images obtained in this paper are more adapted to the object detection task and can assist in improving the precision of detection.