Development of a GPU-accelerated super resolution solver
Aaron L. Paolini, Fernando E. Ortiz, Daniel K. Price, Kyle E. Spagnoli · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
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 highresolution equipment is unavailable or impossible to deploy. In these scenarios, super resolution methods can be applied to recover lost detail. 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, realtime 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 paper 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 Wiener filtering. This accelerated implementation performs at speeds 40 times that of a conventional a CPU implementation, and achieves processing rates suitable for valuable real-time applications.