In precision surgery, accurate imaging and instrument tracking are paramount for successful outcomes. Traditional RGB cameras often suffer from motion blur when capturing high-speed movements, compromising the precision of surgical navigation systems. This study explorses the integration of event-based imaging with traditional RGB cameras to enhance the precision and clarity of surgical navigation systems. Utilizing an event camera, we capture asynchronous pixel-level brightness changes, effectively eliminating motion blur and providing microsecond-level latency. The fusion of event camera data with RGB images significantly improves image quality, aiding in the accurate localization and analysis of surgical instruments. Our algorithm combines these data streams, optimizing parameters to achieve clear and detailed reconstructions, crucial for high-speed surgical procedures. This approach demonstrates substantial potential in improving surgical outcomes, reducing errors, and advancing the development of more effective surgical navigation systems.