We study the intelligent offloading problem for a multiple unmanned aerial vehicle (multi-UAV)-assisted mobile edge computing (MEC) system in a MEC scenario where a natural disaster has damaged the edge server. The study has two steps. First, the task offloading destination is determined by minimizing the total energy consumption of the multiple UAVs in the system. We propose the server selection game-theoretic (SSGT) algorithm and demonstrate its convergence through simulation experiments. Second, we propose an offloading incentive mechanism to price computing resources for a single UAV-MEC server. Considering the UAV’s power consumption and mobile users’ willingness, we model the interaction between the UAV-MEC server and mobile users as a Stackelberg game. We prove the existence of a Nash equilibrium by theoretical analysis and experimental verification and design the multi-round iterative game (MRIG) algorithm based on arithmetic descent to achieve the optimal solution, i.e., the utility tradeoff between the UAV-MEC server and mobile users. Finally, the simulation results show that our proposed scheme can increase the value of overall user satisfaction (SoU) more than other schemes, which proves that the incentive mechanism of resource pricing can supply computing power support for ground mobile users in a UAV-assisted MEC system more effectively.

Most of the existing infrared and visible image fusion algorithms rely on hand-designed or simple convolution-based fusion strategies. However, these methods cannot explicitly model the contextual relationships between infrared and visible images, thereby limiting their robustness. To this end, we propose a novel Transformer-based feature fusion network for robust image fusion that can explicitly model the contextual relationship between the two modalities. Specifically, our fusion network consists of a detail self-attention module to capture the detail information of each modality and a saliency cross attention module to model contextual relationships between the two modalities. Since these two attention modules can obtain the pixel-level global dependencies, the fusion network has a powerful detail representation ability which is critical to the pixel-level image generation task. Moreover, we propose a deformable convolution-based feature align network to address the slight misaligned problem of the source image pairs, which is beneficial for reducing artifacts. Since there is no ground-truth for the infrared and visible image fusion task, it is essential to train the proposed method in a self-supervised manner. Therefore, we design a self-supervised multi-task loss which contains a structure similarity loss, a frequency consistency loss, and a Fourier spectral consistency loss to train the proposed algorithm. Extensive experimental results on four image fusion benchmarks show that our algorithm obtains competitive performance compared to state-of-the-art algorithms.