GPU accelerated adaptive compressed sensing
Somoye, Idris Olansile
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There are presently image sensors based around compressed sensing that apply the fundamental theory to video acquisition; however, these imagers require specialized hardware modules that are not widely available and therefore are not currently practical for video sensing. To deliver a practical image sensor that applies compressive sensing, I propose an imaging system based on a GPU and an off-the-shelf conventional image sensor that takes advantage of parallel computations for efficient transforming of data to the compressed sensing domain. This imaging system, by taking advantage of GPU processing along with straightforward communication methods between the host and the GPU, easily accommodates algorithms that rapidly change the sensing basis, making compressed sensing more applicable despite the general lack of hardware. Measurement results show that the GPU based compressive sensing imaging system delivers a viable and practical imager that is able to quickly compress images, providing a real-time video encoder for low power systems.