Submarine Forward Escape Trunk Cofferdam

The ability to autonomously enhance low-quality or corrupted streaming video data is essential in a number of important defense and civilian scenarios. In military applications, the need to enhance and clean-up corrupted video signal is crucial foroperator-assisted or autonomous terminal guidance of missiles. Quality of video also affects critical decision-making in civilian applications ranging from security to remotely-controlled surgical procedures. The entertainment sector also demandshigh-quality imaging despite low-quality cameras (e.g. web-cam) and under adverse lighting conditions. In spite of the apparent pervasive need for video enhancement techniques, progress has been limited to either human-assisted video clean-up or to veryspecific types of automatic video correction.In our Phase I effort we have developed a limited prototype of RAVE: Real-time Autonomous Video Enhancement system. In Phase II we propose to develop and validate a full-scope prototype of RAVE as a real-time embedded system. We describe the existing suiteof RAVE's video enhancement and artifact detection algorithms. The algorithm selection is autonomously controlled by the situation assessment-based agent, called RAVE Agent. We present a scalable unified software architecture which will function as aframework for testing and rigorous validation in Phase II. We also present our commercialization plans.