基于联邦学习和注意力生成对抗网络的图像高光去除方法
By combining federated learning and attention-based generative adversarial networks, the problems of insufficient model generalization and data privacy in highlight region image processing are solved, achieving efficient highlight removal and detail preservation, thus improving the performance and privacy protection of computer vision tasks.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHONGSHAN FLASHLIGHT POLYTECHNIC
- Filing Date
- 2024-06-08
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies suffer from insufficient model generalization ability and data privacy protection issues in image processing of highlight regions, resulting in incomplete highlight removal, which affects the performance of computer vision tasks. Furthermore, existing methods are prone to exposing sensitive information during training.
A federated learning framework is used to define a global attention generative adversarial network model on a central server. The model is trained and updated by the client, rather than uploading raw data. The attention generative adversarial network is used to focus on features in the highlight region, and the model parameters are optimized using detection loss, reconstruction loss, and hinge loss.
It effectively removes highlight areas while preserving image details, improves the model's representation and generalization capabilities, ensures data privacy and security, and avoids the leakage of sensitive information.
Smart Images
Figure CN118657688B_ABST