## 7 Conclusions and Future Outlook  
In this paper, we proposed a novel deep convolutional generative adversarial network that incorporates self-attention, spectral normalization, and two time-scale update rules, named SAITI-DCGAN, to enhance the synthesis quality of infrared thermal images for aluminum foil sealing. Specifically, a self-attention module was embedded into the generator, and spectral normalization was adopted for both the generator and discriminator. In addition, a two time-scale update rule was applied to coordinate the training of the generator and discriminator. With respect to the functionalities of these modules, we integrated the self-attention into the generator to capture the global dependencies of the images, thereby enhancing the quality and variety of the generated images. Subsequently, we elaborated on the roles and advantages of spectral normalization and how to apply it to generators and discriminators for stabilizing the training process, with the aim of preventing gradient anomalies and pattern crashes. The two time-scale update rule was adopted to expedite the generator's adaptation to the discriminator feedback. This mechanism can improve the balance between the generator and discriminator and enhance convergence.  
In the experiments, we utilized self-collected infrared thermal images of aluminum foil sealing as a dataset for comprehensive verification. We considered both qualitative and quantitative metrics, including the inception score and the Frechet inception distance, to demonstrate the superiority of our proposed SAITI-DCGAN model in terms of image quality and diversity. Compared to traditional GAN and DCGAN models, the images generated by our SAITI-DCGAN were more realistic and of higher quality, as it could capture intricate details and distinctive features of the defect images of aluminum foil sealing. In addition, we also conducted ablation experiments to verify the roles of the self-attention, spectral normalization, and two time-scale update rules in improving the model performance. The results indicate that the combination of these modules significantly enhanced the image generation quality and achieved a balance between stability and training efficiency.  
In conclusion, the proposed SAITI-DCGAN model achieved remarkable results in the quality and diversity of synthesized infrared thermal images for aluminum foil sealing. Our work also provides a strong impetus for the application and innovation of infrared thermal imaging technology. In the future, we will continue to explore and optimize the model, so as to meet the requirements of various practical application scenarios in the industrial field.  
Conceptualization, Z.W. and C.W.; methodology, Z.W. and Y.X.; software, Z.J. and Y.X.; validation, Z.W., C.W. and Z.J.; formal analysis, Z.W. and Y.X.; investigation, C.W.; resources, C.W.; data curation, Z.W. and Y.X.; writing--original draft preparation, Z.W. and Y.X.; writing--review and editing, C.W.; visualization, Z.W. and Y.X.; supervision, Z.J.; project administration, C.W.; funding acquisition, C.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China grant number 52371275.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.