JADD-GAN: A Joint Attention Generative Adversarial Data Fusion Network for Object Detection and Tracking

Guoxia Xu, Hao Wang, Meng Zhao, Hu Zhu

Abstract: Image fusion is the fusion of images captured by different sensors to generate a single image with enhanced information, and fusion technology, as one of the important branches in the field of information fusion, mainly realizes the processing of multi-source image information. However, many commonly used fusion methods usually ignore the visual naturalness and information fidelity of the fused images and lack emphasis on the salient information, which makes the fused images unsuitable for human visual perception. To address these shortcomings of existing methods, in this paper, we propose the Joint Attention and Dual Discriminator Generative Adversarial Data Fusion Network JADD-GAN. In the generator module, to increase the extraction of multi-level information by the network, we firstly adopt a dual encoder structure and give information fusion in the decoder part. Secondly, different discriminators are used for infrared and visible images in order to highlight the thermal radiation information and key textures. The effectiveness of the method is verified by experiments on four datasets, and the results show that the method can effectively highlight the thermal radiation information and key texture details of the fused images, fully demonstrating its great potential and performance in solving the infrared and visible images fusion (IVF) problem.