On-board real-time processing is highly desirable in airborne detection applications. As the data processing
involved here is computationally expensive, typically high power multi-rack system is required to achieve real-time
detection. Use of such hardware on-board is often not feasible in airborne applications due to space
and power constraints. Recently, there has been a lot of interest in the use of Graphics Processing Units
(GPUs) for real-time image processing because of their highly parallel architecture, low cost, and compact
size. With the introduction of high level languages like C/CUDA (Nvidia), CTM (ATI), OpenCL, etc., GPUs
are enjoying a manifold increase in their adoption for general purpose computation. In this paper we present
GPU bound implementations of image registration and multiband RX anomaly detector. We identify the sub-problems,
namely band-to-band registration, phase correlation, feature detection, feature tracking and image
transformation, that can be efficiently parallelized on the SIMD architecture of the GPU. The results from
experiments using these implementation are compared against existing implementation written in Matlab and
C++.
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