Super-resolution reconstruction of images captured from airborne unmanned vehicles

Abstract

Super-resolution (SR) reconstruction refers to the process of combining a sequence of under-sampled and degraded low-resolution (LR) images in order to produce a single high-resolution (HR) image. The LR input images are assumed to have slightly different views of the same scene. In the broad sense, super-resolution techniques attempt to improve spatial resolution by incorporating into the final HR result the additional new details that are revealed in each LR image. This can be the case of the images captured from unmanned aerial vehicles (UAVs). These images must have sufficient overlap to produce an HR image. Additionally, information about the UAV altitude and attitude-rotational parameters yaw, pitch, and roll-that allows us to relate the different images to a common coordinate system is also needed. This extra information can be used to get an SR image of the overlapping area common to all these images. In this paper, we define a metric to determine if there is enough overlap between a set of frames that would allow SR reconstruction. When this overlap exists, we use the set of registered data to reconstruct an SR image.