Infrared and Visible Image Fusion Using Attention- Based Generative Adversarial Networks

At present, deep learning-based fusion methods rely only on convolutional kernels to extract local features, but the limitations of single-scale networks, convolutional kernel size, and network depth cannot provide a sufficient number of multi-scale and global image characteristics. Therefore, here we propose an infrared and visible image fusion method using attention-based generative adversarial networks. This study uses a generator consisting of an encoder and decoder, and two discriminators. The multi-scale module and channel self-attention mechanism are designed in the encoder, which can effectively extract multi-scale features and establish the dependency between the long ranges of feature channels, thus enhancing the global characteristics of multi-scale features. In addition, two discriminators are constructed to establish an adversarial relationship between the fused image and the source images to preserve more detailed information. The experimental results demonstrate that the proposed method is superior to other typical methods in both subjective and objective evaluations.