Research on Image Restoration of Border Personnel Based on Big Data
With the rapid progress of face recognition and video surveillance technology,portrait detection has become crucial in border security.Image quality,often affected by blur and noise,poses a challenge to the monitoring efficiency.To this end,this study introduces a Transformer-based fuzzy face repair network,aiming to improve the damaged image quality and enhance the performance of the border monitoring system.The performance of the model on the key per-formance indicators LPIPS and SSIM was evaluated by performing experiments on the Colum-bia Gaze dataset.The results show that the network significantly improves the image clarity,provides solid technical support for the real-time warning of the images of border personnel,sig-nificantly improves the reliability and working efficiency of the monitoring system,and shows the application potential in the field of border security monitoring.
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