Heat haze and atmospheric turbulence can significantly reduce the quality of visual imagery and degrade the effectiveness of conventional approaches to detection and classification which, in turn, makes scene interpretation and analysis extremely difficult. This is particularly important for natural history filmmaking as well as for security applications. This new 2 year project. Led by Pui Anantrasirichi, David Bull and Alin Achim, will address this challenge using modern data driven machine learning methods. It will develop real-time video restoration techniques that mitigate spatio-temporal distortions and thus improve visual interpretation of the scene by a human observer. It will also assist decision making by implementing and evaluating real-time object recognition and tracking using the restored video.