Real-time Generative Video Restoration at the Tactical Edge

Real-time access to clear video intelligence is critical for military operations, yet bandwidth constraints and network limitations often result in degraded video quality when tactical decisions are most urgent. This Phase I effort proposes a novel generative video restoration system that learns from the actual scene being transmitted to reconstruct authentic detail in degraded video regions - a fundamental departure from traditional AI enhancement approaches that hallucinate details based on generic training data. The proposed system builds upon the proven Generative Video Codec (GVC) technology, which achieves structural similarity (SSIM) scores above 0.875 in critical regions during normal operation - the threshold at which operators can clearly distinguish mission-critical features. When bandwidth constraints force video quality below this threshold, the generative restoration system activates, leveraging multiple quality levels naturally present within the GVC stream to train scene-specific restoration models directly at the tactical edge. Research objectives focus on achieving real-time generative restoration on standard laptop hardware without cloud dependencies. Key investigation areas include optimizing the training process for real-time operation, ensuring authentic detail reconstruction without hallucination, and validating performance across various operational scenarios. The Phase I effort will analyze product-market fit within CDAO mission sets, particularly for applications requiring immediate access to high-quality video intelligence for critical decision-making. Success in Phase I would establish the foundation for Phase II prototype development of an edge-based video restoration system that maintains both operational security and independence from cloud infrastructure. This capability stands to transform access to video intelligence in contested environments, enabling confident decisions based on clear video when and where operators need it most.