Super-Resolution Reconstruction by Image Fusion and Application to Surveillance Videos Captured by Small Unmanned Aircraft Systems

In practice, surveillance video captured by a small Unmanned Aircraft System (UAS) digital imaging payload is almost always blurred and degraded because of limits of the imaging equipment and less than ideal atmospheric conditions. Small UAS vehicles typically have wingspans of less than four meters and payload carrying capacities of less than 50 kilograms, which results in a high vibration environment due to winds buffeting the aircraft and thus poorly stabilized video that is not necessarily pointed at a target of interest. Superresolution image reconstruction can reconstruct a highly-resolved image of a scene from either a single image or a time series of low-resolution images based on image registration and fusion between different video frames [1, 6, 8, 18, 20, 27]. By fusing several subpixelregistered, low-resolution video frames, we can reconstruct a high-resolution panoramic image and thus improve imaging system performance. There are four primary applications for super-resolution image reconstruction: 1. Automatic Target Recognition: The interesting target is hard to identify and recognize under degraded videos and images. For a series of low-resolution images captured by a small UAS vehicle flown over an area under surveillance, we need to perform super-resolution to enhance image quality and automatically recognize targets of interest. 2. Remote Sensing: Remote sensing observes the Earth and helps monitor vegetation health, bodies of water, and climate change based on image data gathered by wireless equipments over time. We can gather additional information on a given area by increasing the spatial image resolution. 3. Environmental Monitoring: Related to remote sensing, environmental monitoring helps determine if an event is unusual or extreme, and to assist in the development of an appropriate experimental design for monitoring a region over time. With the 22