Low-altitude flight images are prone to dynamic blur due to platform disturbances, complex scene motion, and dynamic exposure variations. This degradation significantly limits the performance of subsequent tasks such as target recognition and geographic information extraction. Starting from the dynamic blur characteristics of low-altitude flight images, this paper systematically analyzes scene image features, classification of blur causes, and mathematical modeling foundations. It focuses on constructing a dynamic blur restoration technology framework and discusses key components including image preprocessing, blur feature extraction, core restoration modules, and result optimization and evaluation. Based on the latest advances in deep learning, the research integrates physical priors with data-driven methods to provide a systematic technical approach for dynamic blur restoration in low-altitude flight images, helping to improve the quality of image acquisition and the reliability of low-altitude observation systems.