To integrate the PIE framework with high-level vision models for end-to-end joint optimization and enhanced performance in real-world applications, a multi-task learning approach can be employed. By combining the low-level vision enhancement tasks with high-level vision tasks such as semantic segmentation or face detection, the model can learn to optimize both tasks simultaneously, leveraging the shared features and representations learned during training.
One approach could involve creating a unified network architecture that incorporates the PIE framework for low-light enhancement as a pre-processing step before feeding the enhanced images into the high-level vision models. The entire system can be trained end-to-end, allowing the model to learn the best representations for both low-level and high-level tasks in a cohesive manner.
Furthermore, transfer learning techniques can be utilized to fine-tune the joint model on specific datasets or tasks, enabling it to adapt and specialize for different applications. By leveraging the complementary strengths of low-level and high-level vision models, the integrated PIE framework can achieve superior performance in real-world vision applications.