Abstract

The acquisition of high-resolution imagery is necessary in a wide variety of fields, such as intelligence gathering, surveillance, and other defense applications. The quality of footage typically determines the usefulness of the obtained information; yet, the use of low-resolution imaging devices may be unavoidable under circumstances where high-resolution equipment is unavailable or impossible to deploy. In these scenarios, super resolution methods can be applied to recover lost detail. Super resolution refers broadly to a class of signal processing techniques that enhance the resolution of data beyond that which is provided by the imaging system. These methods generally use computationally intense routines to process a series of low-resolution input frames in order to generate a higher-resolution output. Because of the algorithms' computational intensity, real-time operation for moderately-sized frames cannot be realized using general-purpose CPU technology. Modern graphics processing units (GPUs) offer computational performance that far exceeds current CPU technology, allowing real-time operation to be achieved. This thesis presents the development of a GPU-accelerated super resolution implementation. The algorithm presented here employs gradient-based registration, weighted nearest neighbor (WNN) interpolation techniques, and Weiner filtering. This accelerated implementation performs at speeds 40 times that of a conventional CPU implementation and achieves processing rates suitable for valuable real-time applications.